Predictive map generation based on seeding characteristics and control

ABSTRACT

One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.

FIELD OF THE DESCRIPTION

The present description relates to agricultural machines, forestrymachines, construction machines, and turf management machines.

BACKGROUND

There are a wide variety of different types of agricultural machines.Some agricultural machines include harvesters, such as combineharvesters, sugar cane harvesters, cotton harvesters, self-propelledforage harvesters, and windrowers. Some harvesters can also be fittedwith different types of heads to harvest different types of crops.

The fields upon which the different types of agricultural machinesoperate can have a variety of characteristics. Each of the differentcharacteristics of the fields upon which the agricultural machinesoperate can vary across the field. Agricultural harvesters may operatedifferently in different areas of the field, depending on thecharacteristics in those areas.

The discussion above is merely provided for general backgroundinformation and is not intended to be used as an aid in determining thescope of the claimed subject matter. SUMMARY

One or more information maps are obtained by an agricultural workmachine. The one or more information maps map one or more agriculturalcharacteristic values at different geographic locations of a field. Anin-situ sensor on the agricultural work machine senses an agriculturalcharacteristic as the agricultural work machine moves through the field.A predictive map generator generates a predictive map that predicts apredictive agricultural characteristic at different locations in thefield based on a relationship between the values in the one or moreinformation maps and the agricultural characteristic sensed by thein-situ sensor. The predictive map can be output and used in automatedmachine control.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter. The claimed subject matter is not limited to examples that solveany or all disadvantages noted in the background.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a partial pictorial, partial schematic illustration of oneexample of a combine harvester.

FIG. 2 is a block diagram showing some portions of an agriculturalharvester in more detail, according to some examples of the presentdisclosure.

FIGS. 3A-3B (collectively referred to herein as FIG. 3) show a flowdiagram illustrating an example of operation of an agriculturalharvester in generating a map.

FIG. 4A is a block diagram showing one example of a predictive modelgenerator and a predictive map generator.

FIG. 4B is a block diagram showing example in-situ sensors.

FIG. 5 is a flow diagram showing an example of operation of anagricultural harvester in receiving a seeding map, detecting acharacteristic, and generating a functional predictive map for use incontrolling the agricultural harvester during a harvesting operation.

FIG. 6 is a block diagram showing one example of an agriculturalharvester in communication with a remote server environment.

FIGS. 7-9 show examples of mobile devices that can be used in anagricultural harvester.

FIG. 10 is a block diagram showing one example of a computingenvironment that can be used in an agricultural harvester and thearchitectures illustrated in previous figures.

DETAILED DESCRIPTION

For the purposes of promoting an understanding of the principles of thepresent disclosure, reference will now be made to the examplesillustrated in the drawings, and specific language will be used todescribe the same. It will nevertheless be understood that no limitationof the scope of the disclosure is intended. Any alterations and furthermodifications to the described devices, systems, methods, and anyfurther application of the principles of the present disclosure arefully contemplated as would normally occur to one skilled in the art towhich the disclosure relates. In particular, it is fully contemplatedthat the features, components, steps, or a combination thereof describedwith respect to one example may be combined with the features,components, steps, or a combination thereof described with respect toother examples of the present disclosure.

The present description relates to using in-situ data taken concurrentlywith an agricultural operation, in combination with prior data, togenerate a predictive map and, more particularly, a predictivecharacteristic map that correlates the in-situ data with the prior datato predict the characteristic indicated by the in-situ data, or arelated characteristic, across the field. In some examples, thepredictive characteristic map can be used to control an agriculturalwork machine, such as an agricultural harvester. Seeding characteristicscan vary across a field. Other agricultural characteristics, such asnon-machine characteristics or machine characteristics may be affectedby or otherwise have some relationship to the various seedingcharacteristics such that the various agricultural characteristics, suchas non-machine characteristics or machine characteristics, may bepredictable in different areas of the field having similar seedingcharacteristics. For example, a yield or a biomass of crop in one areaof the field with known (or estimated) seeding characteristics, may besimilar to a yield or a biomass of crop in another area of the fieldwith known (or estimated) similar seeding characteristics. Yield andbiomass are merely examples, and various other characteristics may bepredictable in different areas of the field based on seedingcharacteristics.

The performance of an agricultural machine may be affected by theagricultural characteristic, and, thus, by predicting the agriculturalcharacteristic across the field, control of the agricultural machine canbe undertaken to optimize the agricultural machine's operation given thepredicted agricultural characteristic. For instance, by predicting thebiomass of crop across the field based on data from a seeding map andin-situ data indicative of the biomass, such as crop height, cropdensity, crop mass, crop volume, or threshing rotor drive force, as wellas number of other characteristics, the position of the header of theagricultural harvester relative to the field surface or the forwardspeed of the agricultural harvester can be adjusted to control athroughput or feed rate of plant material to be processed by theagricultural harvester. These are merely some examples.

Performance of an agricultural harvester may be affected based on anumber of different agricultural characteristics, such as non-machinecharacteristics, for instance characteristics of the field orcharacteristics of plants on the field or a number of different machinecharacteristics of the agricultural harvester, such as machine settings,operating characteristics, or characteristics of machine performance.Sensors on the agricultural harvester can be used in-situ to detectthese agricultural characteristics or to detect values indicative ofthese agricultural characteristics, and the agricultural harvester canbe controlled in various ways based on these agriculturalcharacteristics or characteristics related to the agriculturalcharacteristics detected by in-situ sensors.

A seeding map illustratively maps seeding characteristics acrossdifferent geographic locations in a field of interest. These seedingmaps are typically collected from past seed planting operations on thefield. In some examples, the seeding map may be derived from controlsignals used by a seeder when planting the seeds or from sensors on theseeder, such as sensors that confirm a seed was delivered to a furrowgenerated by the seeder. Seeders can include geographic position sensorsthat geolocate the locations of where the seeds were planted as well astopographic sensors that generate topographic information of the field.For instance, the topographical sensors may include GPS, laser levelers,inclinometer/odometer pairs, local radio triangulation, as well asvarious other systems for generating topographic information. Theinformation generated during a previous seed planting operation can beused to determine various seeding characteristics, such as location(e.g., geographic location of the planted seeds in the field), spacing(e.g., the spacing between the individual seeds, the spacing between theseed rows, or both), population (which can be derived from spacingcharacteristics), seed orientation (e.g., seed orientation in a trenchor orientation of the seed rows), depth (e.g., seed depth or furrowdepth), dimensions (such as seed size), or genotype (such as seedspecies, seed hybrid, seed cultivar, genotype etc.). A variety of otherseeding characteristics may be determined as well. In some examples,seeding maps may comprise information about the seedbed in which theseed is deposited such as soil moisture, soil temperature, soilconstituents such as soil organic matter.

Alternatively, or in addition to data from a prior operation, variousseeding characteristics on the seeding maps can be generated based ondata from third parties, such as third-party seed vendors that providethe seeds for the seed planting operation. These third parties mayprovide various data that indicates various seeding characteristics, forexample, dimension data, such as seed size, or genotype data, such asseed species, seed hybrid, seed variety, or seed cultivar. Additionally,seed vendors can provide various data relative to particular plantcharacteristics of the resultant plants of each different seed genotype.For example, data on plant growth, such as stalk diameter, ear size,plant height, plant mass, etc., plant response to weather conditions,plant response to applied substances, such as herbicide, fungicide,pesticide, insecticide, fertilizer, shattering characteristics, dry downcharacteristics, crop response to weather, crop response to pests, andcrop response to fungus etc., plant response to pests, fungus, weeds,disease, etc., as well as any number of other plant characteristics. Itshould be noted that plant response data can include data indicative ofplant resistance to various conditions and characteristic, for instance,plant resistance to applied substances, plant resistance to weatherconditions, plant resistance to pests, fungus, weeds, diseases, etc., aswell as plant resistance to a variety of other conditions orcharacteristics.

Alternatively, or in addition to the data from a prior operation or froma third party, various seeding characteristics on the seeding maps canbe generated based on various user or operator input data, for instance,operator or user input data indicative of various seedingcharacteristics, such as location, depth, orientation, spacing,dimensions, genotype, as well as various other seeding characteristics.

In some examples, a seeding map may be derived from sensor readings ofone or more bands of electromagnetic radiation reflected by the seeds orseedbed. Without limitation, these bands may be in the microwave,infrared, visible, or ultraviolet portions of the electromagneticspectrum.

These are merely some examples of the ways in which seeding maps can begenerated and provided in current systems. Those skilled in the art willappreciate that seeding maps can be generated in a variety of ways andthat the scope of the present disclosure is not limited to the examplesprovided herein.

In some examples, the seeding characteristics provided by a seeding mapmay have a relationship to or otherwise affect various othercharacteristics. By knowing the seeding characteristics across thefield, various other characteristics across the field can be predicted.During a harvesting operation, in-situ sensors on the agriculturalharvesting machine can be used to detect various characteristics of theenvironment in which the agricultural harvesting machine is operating orvarious machine characteristics of the agricultural harvester. Thecharacteristics sensed by the in-situ sensors corresponding to one ormore geographic locations of the field can be used, along with thegeoreferenced seeding characteristic provided by the seeding map, topredict the characteristics at other geographic locations across thefield. For instance, by knowing the seed population in one or moregeographic locations of the field, as obtained from the seeding map, andthe resultant yield at those one or more geographic locations, asindicated by the in-situ sensors, yield in other geographic locationsacross the field, for instance, other geographic locations where thesame population of seed was planted, can be predicted. The combinationof seed population and yield is merely one example. A relationshipbetween various seeding characteristics and various characteristicssensed by in-situ sensors can be modeled to predict the characteristicsensed by the in-situ sensors across the field.

The present discussion thus proceeds with respect to systems thatreceive a seeding map of a field or a map generated on the basis of aprior operation, such as a prior seed planting operation, and also usean in-situ sensor to detect a variable indicative of one or morecharacteristics during a harvesting operation, such as an agriculturalcharacteristic, for instance, a non-machine characteristic, such ascharacteristics of the field or plants on the field, as well as machinecharacteristics such as a machine setting, an operating characteristic,or machine performance data. It will be noted, however, that the in-situsensor can detect a variable indicative of any of a number ofcharacteristics and is not limited to the characteristics describedherein. An agricultural characteristic is any of a number ofcharacteristics which may affect an agricultural operation, such as aharvesting operation. The systems generate a model that models arelationship between the seeding characteristic values on the seedingmap or the values on the map generated from the prior operation and theoutput values from the in-situ sensor. The model is used to generate afunctional predictive map that predicts the characteristic indicated bythe output values from the in-situ sensor at different locations in thefield. The functional predictive map, generated during the harvestingoperation, can be presented to an operator or other user, used inautomatically controlling an agricultural harvester during theharvesting operation, or both.

FIG. 1 is a partial pictorial, partial schematic, illustration of aself-propelled agricultural harvester 100. In the illustrated example,agricultural harvester 100 is a combine harvester. Further, althoughcombine harvesters are provided as examples throughout the presentdisclosure, it will be appreciated that the present description is alsoapplicable to other types of harvesters, such as cotton harvesters,sugarcane harvesters, self-propelled forage harvesters, windrowers, orother agricultural work machines. Consequently, the present disclosureis intended to encompass the various types of harvesters described andis, thus, not limited to combine harvesters. Moreover, the presentdisclosure is directed to other types of work machines, such asagricultural seeders and sprayers, construction equipment, forestryequipment, and turf management equipment where generation of apredictive map may be applicable. Consequently, the present disclosureis intended to encompass these various types of harvesters and otherwork machines and is, thus, not limited to combine harvesters.

As shown in FIG. 1, agricultural harvester 100 illustratively includesan operator compartment 101, which can have a variety of differentoperator interface mechanisms, for controlling agricultural harvester100. Agricultural harvester 100 includes front-end equipment, such as aheader 102, and a cutter generally indicated at 104. Agriculturalharvester 100 also includes a feeder house 106, a feed accelerator 108,and a thresher generally indicated at 110. The feeder house 106 and thefeed accelerator 108 form part of a material handling subsystem 125.Header 102 is pivotally coupled to a frame 103 of agricultural harvester100 along pivot axis 105. One or more actuators 107 drive movement ofheader 102 about axis 105 in the direction generally indicated by arrow109. Thus, a vertical position of header 102 (the header height) aboveground 111 over which the header 102 travels is controllable byactuating actuator 107. While not shown in FIG. 1, agriculturalharvester 100 may also include one or more actuators that operate toapply a tilt angle, a roll angle, or both to the header 102 or portionsof header 102. Tilt refers to an angle at which the cutter 104 engagesthe crop. The tilt angle is increased, for example, by controllingheader 102 to point a distal edge 113 of cutter 104 more toward theground. The tilt angle is decreased by controlling header 102 to pointthe distal edge 113 of cutter 104 more away from the ground. The rollangle refers to the orientation of header 102 about the front-to-backlongitudinal axis of agricultural harvester 100.

Thresher 110 illustratively includes a threshing rotor 112 and a set ofconcaves 114. Further, agricultural harvester 100 also includes aseparator 116. Agricultural harvester 100 also includes a cleaningsubsystem or cleaning shoe (collectively referred to as cleaningsubsystem 118) that includes a cleaning fan 120, chaffer 122, and sieve124. The material handling subsystem also includes discharge beater 126,tailings elevator 128, clean grain elevator 130, as well as unloadingauger 134 and spout 136. The clean grain elevator moves clean grain intoclean grain tank 132. Agricultural harvester 100 also includes a residuesubsystem 138 that can include chopper 140 and spreader 142.Agricultural harvester 100 also includes a propulsion subsystem thatincludes an engine that drives ground engaging components 144, such aswheels or tracks. In some examples, a combine harvester within the scopeof the present disclosure may have more than one of any of thesubsystems mentioned above. In some examples, agricultural harvester 100may have left and right cleaning subsystems, separators, etc., which arenot shown in FIG. 1.

In operation, and by way of overview, agricultural harvester 100illustratively moves through a field in the direction indicated by arrow147. As agricultural harvester 100 moves, header 102 (and the associatedreel 164) engages the crop to be harvested and gathers the crop towardcutter 104. An operator of agricultural harvester 100 can be a localhuman operator, a remote human operator, or an automated system. Anoperator command is a command by an operator. The operator ofagricultural harvester 100 may determine one or more of a heightsetting, a tilt angle setting, or a roll angle setting for header 102.For example, the operator inputs a setting or settings to a controlsystem, described in more detail below, that controls actuator 107. Thecontrol system may also receive a setting from the operator forestablishing the tilt angle and roll angle of the header 102 andimplement the inputted settings by controlling associated actuators, notshown, that operate to change the tilt angle and roll angle of theheader 102. The actuator 107 maintains header 102 at a height aboveground 111 based on a height setting and, where applicable, at desiredtilt and roll angles. Each of the height, roll, and tilt settings may beimplemented independently of the others. The control system responds toheader error (e.g., the difference between the height setting andmeasured height of header 104 above ground 111 and, in some examples,tilt angle and roll angle errors) with a responsiveness that isdetermined based on a selected sensitivity level. If the sensitivitylevel is set at a greater level of sensitivity, the control systemresponds to smaller header position errors, and attempts to reduce thedetected errors more quickly than when the sensitivity is at a lowerlevel of sensitivity.

Returning to the description of the operation of agricultural harvester100, after crops are cut by cutter 104, the severed crop material ismoved through a conveyor in feeder house 106 toward feed accelerator108, which accelerates the crop material into thresher 110. The cropmaterial is threshed by threshing rotor 112 rotating the crop againstconcaves 114. The threshed crop material is moved by a separator rotorin separator 116 where a portion of the residue is moved by dischargebeater 126 toward the residue subsystem 138. The portion of residuetransferred to the residue subsystem 138 is chopped by residue chopper140 and spread on the field by spreader 142. In other configurations,the residue is released from the agricultural harvester 100 in awindrow. In other examples, the residue subsystem 138 can include weedseed eliminators (not shown) such as seed baggers or other seedcollectors, or seed crushers or other seed destroyers.

Grain falls to cleaning subsystem 118. Chaffer 122 separates some largerpieces of material from the grain, and sieve 124 separates some of finerpieces of material from the clean grain. Clean grain falls to an augerthat moves the grain to an inlet end of clean grain elevator 130, andthe clean grain elevator 130 moves the clean grain upwards, depositingthe clean grain in clean grain tank 132. Residue is removed from thecleaning subsystem 118 by airflow generated by cleaning fan 120.Cleaning fan 120 directs air along an airflow path upwardly through thesieves and chaffers. The airflow carries residue rearwardly inagricultural harvester 100 toward the residue handling subsystem 138.

Tailings elevator 128 returns tailings to thresher 110 where thetailings are re-threshed. Alternatively, the tailings also may be passedto a separate re-threshing mechanism by a tailings elevator or anothertransport device where the tailings are re-threshed as well.

FIG. 1 also shows that, in one example, agricultural harvester 100includes ground speed sensor 146, one or more separator loss sensors148, a clean grain camera 150, a forward looking image capture mechanism151, which may be in the form of a stereo or mono camera, and one ormore loss sensors 152 provided in the cleaning subsystem 118.

Ground speed sensor 146 senses the travel speed of agriculturalharvester 100 over the ground. Ground speed sensor 146 may sense thetravel speed of the agricultural harvester 100 by sensing the speed ofrotation of the ground engaging components (such as wheels or tracks), adrive shaft, an axel, or other components. In some instances, the travelspeed may be sensed using a positioning system, such as a globalpositioning system (GPS), a dead reckoning system, a long rangenavigation (LORAN) system, or a wide variety of other systems or sensorsthat provide an indication of travel speed.

Loss sensors 152 illustratively provide an output signal indicative ofthe quantity of grain loss occurring in both the right and left sides ofthe cleaning subsystem 118. In some examples, sensors 152 are strikesensors which count grain strikes per unit of time or per unit ofdistance traveled to provide an indication of the grain loss occurringat the cleaning subsystem 118. The strike sensors for the right and leftsides of the cleaning subsystem 118 may provide individual signals or acombined or aggregated signal. In some examples, sensors 152 may includea single sensor as opposed to separate sensors provided for eachcleaning subsystem 118.

Separator loss sensor 148 provides a signal indicative of grain loss inthe left and right separators, not separately shown in FIG. 1. Theseparator loss sensors 148 may be associated with the left and rightseparators and may provide separate grain loss signals or a combined oraggregate signal. In some instances, sensing grain loss in theseparators may also be performed using a wide variety of different typesof sensors as well.

Agricultural harvester 100 may also include other sensors andmeasurement mechanisms. For instance, agricultural harvester 100 mayinclude one or more of the following sensors: a header height sensorthat senses a height of header 102 above ground 111; stability sensorsthat sense oscillation or bouncing motion (and amplitude) ofagricultural harvester 100; a residue setting sensor that is configuredto sense whether agricultural harvester 100 is configured to chop theresidue, produce a windrow, etc.; a cleaning shoe fan speed sensor tosense the speed of cleaning fan 120; a concave clearance sensor thatsenses clearance between the threshing rotor 112 and concaves 114; athreshing rotor speed sensor that senses a rotor speed of threshingrotor 112; a force sensor that senses a force required to drivethreshing rotor 112, such as a pressure sensor that senses a fluidpressure (e.g., hydraulic fluid, air, etc.) used to drive threshingrotor 112 or a torque sensor that senses a torque used to drivethreshing rotor 112; a chaffer clearance sensor that senses the size ofopenings in chaffer 122; a sieve clearance sensor that senses the sizeof openings in sieve 124; a material other than grain (MOG) moisturesensor that senses a moisture level of the MOG passing throughagricultural harvester 100, such as a capacitive sensor; one or moremachine setting sensors configured to sense various configurablesettings of agricultural harvester 100; a machine orientation sensorthat senses the orientation of agricultural harvester 100; and cropproperty sensors that sense a variety of different types of cropproperties, such as crop type, crop moisture, crop height, crop density,crop mass, crop volume, stalk characteristics, kernel characteristics,husk characteristics, ear characteristics, crop color characteristics,including color characteristics of crop components, such as ear color,husk color, cob color, grain color, etc. Crop property sensors may alsobe configured to sense characteristics of crop constituents, such as anamount of constituent (such as oil, starch, protein, and other chemicalclasses) contained in crop material, or contained in components of thecrop plant, such as grain. Crop property sensors may also be configuredto sense characteristics of the severed crop material as the cropmaterial is being processed by agricultural harvester 100. For example,in some instances, the crop property sensors may sense grain qualitysuch as broken grain, MOG levels; grain constituents such as starchesand protein; and grain feed rate as the grain travels through the feederhouse 106, clean grain elevator 130, or elsewhere in the agriculturalharvester 100. The crop property sensors may also sense the feed rate ofbiomass through feeder house 106, through the separator 116 or elsewherein agricultural harvester 100. The crop property sensors may also sensethe feed rate as a mass flow rate of grain through elevator 130 orthrough other portions of the agricultural harvester 100 or provideother output signals indicative of other sensed variables. Crop propertysensors can include one or more yield sensors that sense crop yieldbeing harvested by the agricultural harvester.

Prior to describing how agricultural harvester 100 generates afunctional predictive characteristic map and uses the functionalpredictive characteristic map for control, a brief description of someof the items on agricultural harvester 100 and their respectiveoperations will first be described.

The description of FIGS. 2 and 3 describe receiving a general type ofprior information map and combining information from the priorinformation map with a georeferenced sensor signal generated by anin-situ sensor, where the sensor signal is indicative of one or moreagricultural characteristics, such as one or more non-machinecharacteristics or one or more machine characteristics of agriculturalharvester 100. A non-machine characteristic is any agriculturalcharacteristic that is not related to a machine, such as agriculturalharvester 100. Non-machine characteristics may include a number ofcharacteristics, such as characteristics of the field. Characteristicsof the field may include, without limitation, surface characteristicssuch as topography, slope, surface quality, etc.; weed characteristicssuch as weed intensity, weed type, etc.; characteristics of soilproperties such as soil type, soil moisture, soil cover, soil structure,etc.; characteristics of crop properties such as, crop population, cropheight, crop volume, crop mass, crop moisture, crop density, crop state,stalk characteristics (such as stalk thickness or strength), huskcharacteristics, color data (such as husk color or cob color), shattercharacteristics, yield characteristics, dry down characteristics, pestresponse characteristics, drought response characteristics, weatherresponse characteristics, etc.; or characteristics of grain propertiessuch as grain moisture, grain size, grain test weight, kernelcharacteristics (such as kernel size or weight, etc.). Other non-machinecharacteristics are also within the scope of the present disclosure. Amachine characteristic is any agricultural characteristic which isrelated to a machine, such as agricultural harvester 100. Machinecharacteristics may include a number of characteristics, such as variousmachine settings or operating characteristics such as ground speed, reelsettings (such as reel height or speed), fan speed settings, deck platespacing or position, stalk roll speeds, header height, headerorientation, machine heading, threshing rotor drive force, or engineload. Machine characteristics may also include other machine settings oroperating characteristics. Machine characteristics may also includevarious characteristics of machine performance such as loss levels, jobquality, fuel consumption, and power utilization; or other machinecharacteristics. Other machine characteristics are within the scope ofthe present disclosure.

A relationship between the characteristic values obtained from in-situsensor signals and the prior information map values is identified, andthat relationship is used to generate a new functional predictive map. Afunctional predictive map predicts values at different geographiclocations in a field, and one or more of those values may be used forcontrolling a machine, such as one or more subsystems of an agriculturalharvester. In some instances, a functional predictive map can bepresented to a user, such as an operator of an agricultural workmachine, which may be an agricultural harvester. A functional predictivemap may be presented to a user visually, such as via a display,haptically, or audibly. The user may interact with the functionalpredictive map to perform editing operations and other user interfaceoperations. In some instances, a functional predictive map can be usedfor one or more of controlling an agricultural work machine, such as anagricultural harvester, presentation to an operator or other user, andpresentation to an operator or user for interaction by the operator oruser.

After the general approach is described with respect to FIGS. 2 and 3, amore specific approach for generating a functional predictiveagricultural characteristic map that can be presented to an operator oruser, used to control agricultural harvester 100, or both is describedwith respect to FIGS. 4 and 5. Again, while the present discussionproceeds with respect to the agricultural harvester and, particularly, acombine harvester, the scope of the present disclosure encompasses othertypes of agricultural harvesters or other agricultural work machines.

FIG. 2 is a block diagram showing some portions of an exampleagricultural harvester 100. FIG. 2 shows that agricultural harvester 100illustratively includes one or more processors or servers 201, datastore 202, geographic position sensor 204, communication system 206, andone or more in-situ sensors 208 that sense one or more agriculturalcharacteristics of a field concurrent with a harvesting operation. Anagricultural characteristic can include any characteristic that can havean effect of the harvesting operation. Some examples of agriculturalcharacteristics include characteristics of the harvesting machine, thefield, the plants on the field, and the weather. Other types ofagricultural characteristics are also included. The in-situ sensors 208generate values corresponding to the sensed characteristics. Theagricultural harvester 100 also includes a predictive model orrelationship generator (collectively referred to hereinafter as“predictive model generator 210”), predictive map generator 212, controlzone generator 213, control system 214, one or more controllablesubsystems 216, and an operator interface mechanism 218. Theagricultural harvester 100 can also include a wide variety of otheragricultural harvester functionality 220. The in-situ sensors 208include, for example, on-board sensors 222, remote sensors 224, andother sensors 226 that sense characteristics of a field during thecourse of an agricultural operation. Predictive model generator 210illustratively includes a prior information variable-to-in-situ variablemodel generator 228, and predictive model generator 210 can includeother items 230. Control system 214 includes communication systemcontroller 229, operator interface controller 231, a settings controller232, path planning controller 234, feed rate controller 236, header andreel controller 238, draper belt controller 240, deck plate positioncontroller 242, residue system controller 244, machine cleaningcontroller 245, zone controller 247, and control system 214 can includeother items 246. Controllable subsystems 216 include machine and headeractuators 248, propulsion subsystem 250, steering subsystem 252, residuesubsystem 138, machine cleaning subsystem 254, and controllablesubsystems 216 can include a wide variety of other subsystems 256.

FIG. 2 also shows that agricultural harvester 100 can receive priorinformation map 258. As described below, the prior information map 258includes, for example, a seeding map or a map from a prior operation.However, prior information map 258 may also encompass other types ofdata that were obtained prior to a harvesting operation or a map from aprior operation. FIG. 2 also shows that an operator 260 may operate theagricultural harvester 100. The operator 260 interacts with operatorinterface mechanisms 218. In some examples, operator interfacemechanisms 218 may include joysticks, levers, a steering wheel,linkages, pedals, buttons, dials, keypads, user actuatable elements(such as icons, buttons, etc.) on a user interface display device, amicrophone and speaker (where speech recognition and speech synthesisare provided), among a wide variety of other types of control devices.Where a touch sensitive display system is provided, operator 260 mayinteract with operator interface mechanisms 218 using touch gestures.These examples described above are provided as illustrative examples andare not intended to limit the scope of the present disclosure.Consequently, other types of operator interface mechanisms 218 may beused and are within the scope of the present disclosure.

Prior information map 258 may be downloaded onto agricultural harvester100 and stored in data store 202, using communication system 206 or inother ways. In some examples, communication system 206 may be a cellularcommunication system, a system for communicating over a wide areanetwork or a local area network, a system for communicating over a nearfield communication network, or a communication system configured tocommunicate over any of a variety of other networks or combinations ofnetworks. Communication system 206 may also include a system thatfacilitates downloads or transfers of information to and from a securedigital (SD) card or a universal serial bus (USB) card or both.

Geographic position sensor 204 illustratively senses or detects thegeographic position or location of agricultural harvester 100.Geographic position sensor 204 can include, but is not limited to, aglobal navigation satellite system (GNSS) receiver that receives signalsfrom a GNSS satellite transmitter. Geographic position sensor 204 canalso include a real-time kinematic (RTK) component that is configured toenhance the precision of position data derived from the GNSS signal.Geographic position sensor 204 can include a dead reckoning system, acellular triangulation system, or any of a variety of other geographicposition sensors.

In-situ sensors 208 may be any of the sensors described above withrespect to FIG. 1. In-situ sensors 208 include on-board sensors 222 thatare mounted on-board agricultural harvester 100. The in-situ sensors 208also include remote in-situ sensors 224 that capture in-situinformation. In-situ sensors 208 may sense any of a number ofcharacteristics. For example, the in-situ sensors 208 may sense one ormore characteristics of the environment in which agricultural harvester100 operates (e.g., characteristics of the field) or one or more machinecharacteristics of agricultural harvester 100, such as machine settings,operating characteristics, or machine performance data. Such sensors mayinclude, without limitation, soil characteristic sensors; cropcharacteristic sensors; weed characteristic sensors; yield sensors;biomass sensors; tailings sensors; grain quality sensors; internalmaterial distribution sensors; residue sensors; or machinecharacteristics sensors, such as power characteristic sensors, speedsensors, machine orientation (e.g., pitch, roll, or yaw (direction))sensors, machine performance sensors (e.g., fuel consumption sensors,grain loss sensors, etc.). In some examples, in-situ sensors mayinclude, without limitation, a perception sensor (e.g., a forwardlooking mono or stereo camera system and image processing system) andimage sensors that are internal to agricultural harvester 100 (such asthe clean grain camera or cameras mounted to identify characteristics ofvegetation traveling through agricultural harvester 100). In-situ datainclude data taken from a sensor on-board the agricultural harvester ortaken by any sensor where the data are detected during the harvestingoperation. Some other examples of in-situ sensors 208 are shown in FIG.4B.

Predictive model generator 210 generates a model that is indicative of arelationship between the values sensed by the in-situ sensor 208 and avalue mapped to the field by the prior information map 258. For example,if the prior information map 258 maps a seeding characteristic value todifferent locations in the field, and the in-situ sensor 208 senses avalue indicative of biomass, then prior information variable-to-in-situvariable model generator 228 generates a predictive biomass model thatmodels the relationship between the seeding characteristic value and thebiomass value. This is because various seeding characteristics can beindicative of a resultant biomass of plants on the field of interest.For example, the spacing (such as spacing between seeds in a common rowor spacing between rows) can be indicative of a vegetation density or avegetation population. Seeding characteristics and biomass are merelyexamples, and seeding characteristics may relate to othercharacteristics sensed by one or more in-situ sensors 208 upon whichpredictive model generator 210 may generate a model.

The predictive model can also be generated based on seedingcharacteristic values from the prior information map 258 and multiplein-situ data values generated by in-situ sensors 208. Then, predictivemap generator 212 uses the predictive model generated by predictivemodel generator 210 to generate a functional predictive map 263 thatpredicts the value of a characteristic, such as biomass or a biomasscharacteristic, sensed by the in-situ sensors 208 at different locationsin the field based upon the prior information map 258.

In an example in which prior information map 258 is a seeding map andin-situ sensor 208 senses a value indicative of an agriculturalcharacteristic, predictive map generator 212 can use the seedingcharacteristic values in prior information map 258 and the modelgenerated by predictive model generator 210 to generate a functionalpredictive map 263 that predicts the agricultural characteristic atdifferent locations in the field. Predictive map generator thus outputsa predictive map 264.

In some examples, the type of values in the functional predictive map263 may be the same as the in-situ data type sensed by the in-situsensors 208. In some instances, the type of values in the functionalpredictive map 263 may have different units from the data sensed by thein-situ sensors 208. In some examples, the type of values in thefunctional predictive map 263 may be different from the data type sensedby the in-situ sensors 208 but have a relationship to the type of datatype sensed by the in-situ sensors 208. For example, in some examples,the data type sensed by the in-situ sensors 208 may be indicative of thetype of values in the functional predictive map 263. In some examples,the type of data in the functional predictive map 263 may be differentthan the data type in the prior information map 258. In some instances,the type of data in the functional predictive map 263 may have differentunits from the data in the prior information map 258. In some examples,the type of data in the functional predictive map 263 may be differentfrom the data type in the prior information map 258 but has arelationship to the data type in the prior information map 258. Forexample, in some examples, the data type in the prior information map258 may be indicative of the type of data in the functional predictivemap 263. In some examples, the type of data in the functional predictivemap 263 is different than one of, or both of the in-situ data typesensed by the in-situ sensors 208 and the data type in the priorinformation map 258. In some examples, the type of data in thefunctional predictive map 263 is the same as one of, or both of, of thein-situ data type sensed by the in-situ sensors 208 and the data type inprior information map 258. In some examples, the type of data in thefunctional predictive map 263 is the same as one of the in-situ datatype sensed by the in-situ sensors 208 or the data type in the priorinformation map 258, and different than the other.

As shown in FIG. 2, predictive map 264 predicts the value of a sensedcharacteristic (sensed by in-situ sensors 208) or the value of acharacteristic related to the sensed characteristic at various locationsacross the field based upon a prior information value in priorinformation map 258 at those locations and using the predictive model.For example, if predictive model generator 210 has generated apredictive model indicative of a relationship between a seedingcharacteristic value and crop state, then, given the seedingcharacteristic value at different locations across the field, predictivemap generator 212 generates a predictive map 264 that predicts the valueof the crop state at different locations across the field. The seedingcharacteristic at those locations, obtained from the seeding map, andthe relationship between the seeding characteristic value and cropstate, obtained from the predictive model, are used to generate thepredictive map 264. This is because various seeding characteristics canbe indicative of a resultant crop state of crops on the field. Forinstance, the genotype of the seed planted (such as the seed hybrid) canaffect the resultant crop state. For instance, different hybrids ofcrops are more, or less susceptible to being in a downed state, such asfrom green snap, where stalks are broken due to strong winds. Seedingcharacteristics and crop state are provided merely as examples. Seedingcharacteristics may relate to various other characteristics sensed byone or more in-situ sensors 208 upon which predictive model generator210 may generate a model. Predictive model generator 210 can generate apredictive model indicative of a relationship between a seedingcharacteristic value and any of a number of characteristics sensed byin-situ sensors 208 or any of a number characteristics related to thesensed characteristic, and predictive map generator 212 can generate apredictive map 264 that predicts the value of the characteristic atdifferent locations across the field. The seeding characteristic value,obtained from the seeding map at those locations, and the relationshipbetween the seeding characteristic value and the characteristic sensedby in-situ sensors 208, obtained from the predictive model, can be usedto generate the predictive map 264.

Some variations in the data types that are mapped in the priorinformation map 258, the data types sensed by in-situ sensors 208, andthe data types predicted on the predictive map 264 will now bedescribed.

In some examples, the data type in the prior information map 258 isdifferent from the data type sensed by in-situ sensors 208. Yet, thedata type in the predictive map 264 is the same as the data type sensedby the in-situ sensors 208. For instance, the prior information map 258may be a seeding map, and the variable sensed by the in-situ sensors 208may be yield. In such an example, the predictive map 264 may be apredictive yield map that maps predicted yield values to differentgeographic locations in the field. In another example, the priorinformation map 258 may be a seeding map, and the variable sensed by thein-situ sensors 208 may be crop height. In this example, the predictivemap 264 may be a predictive crop height map that maps predicted cropheight values to different geographic locations in the field.

Also, in some examples, the data type in the prior information map 258is different from the data type sensed by in-situ sensors 208, and thedata type in the predictive map 264 is different from both the data typein the prior information map 258 and the data type sensed by the in-situsensors 208. For instance, the prior information map 258 may be aseeding map, and the variable sensed by the in-situ sensors 208 may becrop height. In such an example, the predictive map 264 may be apredictive biomass map that maps predicted biomass to differentgeographic locations in the field. predictive map 264

In some examples, the prior information map 258 is from a prior passthrough the field during a prior operation and the data type isdifferent from the data type sensed by in-situ sensors 208, yet the datatype in the predictive map 264 is the same as the data type sensed bythe in-situ sensors 208. For instance, the prior information map 258 maybe a seed population map generated during planting, and the variablesensed by the in-situ sensors 208 may be stalk size. The predictive map264 may then be a predictive stalk size map that maps predicted stalksize values to different geographic locations in the field. In anotherexample, the prior information map 258 may be a seeding hybrid map, andthe variable sensed by the in-situ sensors 208 may be crop state such asstanding crop or down crop. The predictive map 264 may then be apredictive crop state map that maps predicted crop state values todifferent geographic locations in the field.

In some examples, the prior information map 258 is from a prior passthrough the field during a prior operation and the data type is the sameas the data type sensed by in-situ sensors 208, and the data type in thepredictive map 264 is also the same as the data type sensed by thein-situ sensors 208. For instance, the prior information map 258 may bea yield map generated during a previous year, and the variable sensed bythe in-situ sensors 208 may be yield. The predictive map 264 may then bea predictive yield map that maps predicted yield values to differentgeographic locations in the field. In such an example, the relativeyield differences in the georeferenced prior information map 258 fromthe prior year can be used by predictive model generator 210 to generatea predictive model that models a relationship between the relative yielddifferences on the prior information map 258 and the yield values sensedby in-situ sensors 208 during the current harvesting operation. Thepredictive model is then used by predictive map generator 210 togenerate a predictive yield map.

In another example, the prior information map 258 may be a weedintensity map generated during a prior operation, such as from asprayer, and the variable sensed by the in-situ sensors 208 may be weedintensity. The predictive map 264 may then be a predictive weedintensity map that maps predicted weed intensity values to differentgeographic locations in the field. In such an example, a map of the weedintensities at time of spraying is geo-referenced recorded and providedto agricultural harvester 100 as a prior information map 258 of weedintensity. In-situ sensors 208 can detect weed intensity at geographiclocations in the field and predictive model generator 210 may then builda predictive model that models a relationship between weed intensity attime of harvest and weed intensity at time of spraying. This is becausethe sprayer will have impacted the weed intensity at time of spraying,but weeds may still crop up in similar areas again by harvest. However,the weed areas at harvest are likely to have different intensity basedon timing of the harvest, weather, weed type, among other things.

In some examples, predictive map 264 can be provided to the control zonegenerator 213. Control zone generator 213 groups adjacent portions of anarea into one or more control zones based on data values of predictivemap 264 that are associated with those adjacent portions. A control zonemay include two or more contiguous portions of an area, such as a field,for which a control parameter corresponding to the control zone forcontrolling a controllable subsystem is constant. For example, aresponse time to alter a setting of controllable subsystems 216 may beinadequate to satisfactorily respond to changes in values contained in amap, such as predictive map 264. In that case, control zone generator213 parses the map and identifies control zones that are of a definedsize to accommodate the response time of the controllable subsystems216. In another example, control zones may be sized to reduce wear fromexcessive actuator movement resulting from continuous adjustment. Insome examples, there may be a different set of control zones for eachcontrollable subsystem 216 or for groups of controllable subsystems 216.The control zones may be added to the predictive map 264 to obtainpredictive control zone map 265. Predictive control zone map 265 canthus be similar to predictive map 264 except that predictive controlzone map 265 includes control zone information defining the controlzones. Thus, a functional predictive map 263, as described herein, mayor may not include control zones. Both predictive map 264 and predictivecontrol zone map 265 are functional predictive maps 263. In one example,a functional predictive map 263 does not include control zones, such aspredictive map 264. In another example, a functional predictive map 263does include control zones, such as predictive control zone map 265. Insome examples, multiple crops may be simultaneously present in a fieldif an intercrop production system is implemented. In that case,predictive map generator 212 and control zone generator 213 are able toidentify the location and characteristics of the two or more crops andthen generate predictive map 264 and predictive control zone map 265accordingly.

It will also be appreciated that control zone generator 213 can clustervalues to generate control zones and the control zones can be added topredictive control zone map 265, or a separate map, showing only thecontrol zones that are generated. In some examples, the control zonesmay be used for controlling or calibrating agricultural harvester 100 orboth. In other examples, the control zones may be presented to theoperator 260 and used to control or calibrate agricultural harvester100, and, in other examples, the control zones may be presented to theoperator 260 or another user or stored for later use.

Predictive map 264 or predictive control zone map 265 or both areprovided to control system 214, which generates control signals basedupon the predictive map 264 or predictive control zone map 265 or both.In some examples, communication system controller 229 controlscommunication system 206 to communicate the predictive map 264 orpredictive control zone map 265 or control signals based on thepredictive map 264 or predictive control zone map 265 to otheragricultural harvesters that are harvesting in the same field. In someexamples, communication system controller 229 controls the communicationsystem 206 to send the predictive map 264, predictive control zone map265, or both to other remote systems.

Operator interface controller 231 is operable to generate controlsignals to control operator interface mechanisms 218. The operatorinterface controller 231 is also operable to present the predictive map264 or predictive control zone map 265 or other information derived fromor based on the predictive map 264, predictive control zone map 265, orboth to operator 260. Operator 260 may be a local operator or a remoteoperator. As an example, controller 231 generates control signals tocontrol a display mechanism to display one or both of predictive map 264and predictive control zone map 265 for the operator 260. Controller 231may generate operator actuatable mechanisms that are displayed and canbe actuated by the operator to interact with the displayed map. Theoperator can edit the map by, for example, correcting a characteristicdisplayed on the map, based on the operator's observation. Settingscontroller 232 can generate control signals to control various settingson the agricultural harvester 100 based upon predictive map 264, thepredictive control zone map 265, or both. For instance, settingscontroller 232 can generate control signals to control machine andheader actuators 248. In response to the generated control signals, themachine and header actuators 248 operate to control, for example, one ormore of the sieve and chaffer settings, concave clearance, rotorsettings, cleaning fan speed settings, header height, headerfunctionality, reel speed, reel position, draper functionality (whereagricultural harvester 100 is coupled to a draper header), corn headerfunctionality, internal distribution control and other actuators 248that affect the other functions of the agricultural harvester 100. Pathplanning controller 234 illustratively generates control signals tocontrol steering subsystem 252 to steer agricultural harvester 100according to a desired path. Path planning controller 234 can control apath planning system to generate a route for agricultural harvester 100and can control propulsion subsystem 250 and steering subsystem 252 tosteer agricultural harvester 100 along that route. Feed rate controller236 can control various subsystems, such as propulsion subsystem 250 andmachine actuators 248, to control a feed rate or throughput based uponthe predictive map 264 or predictive control zone map 265 or both. Forinstance, as agricultural harvester 100 approaches an upcoming area ofcrop on the field having a biomass value above a selected threshold,feed rate controller 236 may reduce the speed of machine 100 to maintainconstant feed rate of biomass through the machine. Header and reelcontroller 238 can generate control signals to control a header or areel or other header functionality. Draper belt controller 240 cangenerate control signals to control a draper belt or other draperfunctionality based upon the predictive map 264, predictive control zonemap 265, or both. Deck plate position controller 242 can generatecontrol signals to control a position of a deck plate included on aheader based on predictive map 264 or predictive control zone map 265 orboth, and residue system controller 244 can generate control signals tocontrol a residue subsystem 138 based upon predictive map 264 orpredictive control zone map 265, or both. Machine cleaning controller245 can generate control signals to control machine cleaning subsystem254. For instance, based upon the different types of seeds or weedspassed through machine 100, a particular type of machine cleaningoperation or a frequency with which a cleaning operation is performedmay be controlled. Other controllers included on the agriculturalharvester 100 can control other subsystems based on the predictive map264 or predictive control zone map 265 or both as well.

FIGS. 3A and 3B (collectively referred to herein as FIG. 3) show a flowdiagram illustrating one example of the operation of agriculturalharvester 100 in generating a predictive map 264 and predictive controlzone map 265 based upon prior information map 258.

At 280, agricultural harvester 100 receives prior information map 258.Examples of prior information map 258 or receiving prior information map258 are discussed with respect to blocks 281, 282, 284 and 286. Asdiscussed above, prior information map 258 maps values of a variable,corresponding to a first characteristic, to different locations in thefield, as indicated at block 282. For instance, one prior informationmap may be a seeding map generated during a prior operation or based ondata from a prior operation on the field, such as prior seed plantingoperation performed by a seeder. The data for the prior information map258 may be collected in other ways as well. For instance, the data maybe collected based on aerial images or measured values taken during aprevious year, or earlier in the current growing season, or at othertimes. The information may be based on data detected or gathered inother ways (other than using aerial images) as well. For instance, thedata for the prior information map 258 can be transmitted toagricultural harvester 100 using communication system 206 and stored indata store 202. The data for the prior information map 258 can beprovided to agricultural harvester 100 using communication system 206 inother ways as well, and this is indicated by block 286 in the flowdiagram of FIG. 3. In some examples, the prior information map 258 canbe received by communication system 206.

Upon commencement of a harvesting operation, in-situ sensors 208generate sensor signals indicative of one or more in-situ data valuesindicative of an agricultural characteristic, for example, a non-machinecharacteristic, such as a characteristic of the field, or a machinecharacteristic, such as machine settings, operating characteristics, orcharacteristics of machine performance, as indicated by block 288.Examples of in-situ sensors 208 are discussed with respect to blocks222, 290, and 226. As explained above, the in-situ sensors 208 includeon-board sensors 222; remote in-situ sensors 224, such as UAV-basedsensors flown at a time to gather in-situ data, shown in block 290; orother types of in-situ sensors, designated by in-situ sensors 226. Insome examples, data from on-board sensors is georeferenced usingposition, heading, or speed data from geographic position sensor 204.

Predictive model generator 210 controls the prior informationvariable-to-in-situ variable model generator 228 to generate a modelthat models a relationship between the mapped values contained in theprior information map 258 and the in-situ values sensed by the in-situsensors 208 as indicated by block 292. The characteristics or data typesrepresented by the mapped values in the prior information map 258 andthe in-situ values sensed by the in-situ sensors 208 may be the samecharacteristics or data type or different characteristics or data types.

The relationship or model generated by predictive model generator 210 isprovided to predictive map generator 212. Predictive map generator 212generates a predictive map 264 that predicts a value of thecharacteristic sensed by the in-situ sensors 208 at different geographiclocations in a field being harvested, or a different characteristic thatis related to the characteristic sensed by the in-situ sensors 208,using the predictive model and the prior information map 258, asindicated by block 294.

It should be noted that, in some examples, the prior information map 258may include two or more different maps or two or more different maplayers of a single map. Each map layer may represent a different datatype from the data type of another map layer or the map layers may havethe same data type that were obtained at different times. Each map inthe two or more different maps or each layer in the two or moredifferent map layers of a map maps a different type of variable to thegeographic locations in the field. In such an example, predictive modelgenerator 210 generates a predictive model that models the relationshipbetween the in-situ data and each of the different variables mapped bythe two or more different maps or the two or more different map layers.Similarly, the in-situ sensors 208 can include two or more sensors eachsensing a different type of variable. Thus, the predictive modelgenerator 210 generates a predictive model that models the relationshipsbetween each type of variable mapped by the prior information map 258and each type of variable sensed by the in-situ sensors 208. Predictivemap generator 212 can generate a functional predictive map 263 thatpredicts a value for each sensed characteristic sensed by the in-situsensors 208 (or a characteristic related to the sensed characteristic)at different locations in the field being harvested using the predictivemodel and each of the maps or map layers in the prior information map258.

Predictive map generator 212 configures the predictive map 264 so thatthe predictive map 264 is actionable (or consumable) by control system214. Predictive map generator 212 can provide the predictive map 264 tothe control system 214 or to control zone generator 213 or both. Someexamples of different ways in which the predictive map 264 can beconfigured or output are described with respect to blocks 296, 295, 299and 297. For instance, predictive map generator 212 configurespredictive map 264 so that predictive map 264 includes values that canbe read by control system 214 and used as the basis for generatingcontrol signals for one or more of the different controllable subsystemsof the agricultural harvester 100, as indicated by block 296.

Control zone generator 213 can divide the predictive map 264 intocontrol zones based on the values on the predictive map 264.Contiguously-geolocated values that are within a threshold value of oneanother can be grouped into a control zone. The threshold value can be adefault threshold value, or the threshold value can be set based on anoperator input, based on an input from an automated system, or based onother criteria. A size of the zones may be based on a responsiveness ofthe control system 214, the controllable subsystems 216, based on wearconsiderations, or on other criteria as indicated by block 295.Predictive map generator 212 configures predictive map 264 forpresentation to an operator or other user. Control zone generator 213can configure predictive control zone map 265 for presentation to anoperator or other user. This is indicated by block 299. When presentedto an operator or other user, the presentation of the predictive map 264or predictive control zone map 265 or both may contain one or more ofthe predictive values on the predictive map 264 correlated to geographiclocation, the control zones on predictive control zone map 265correlated to geographic location, and settings values or controlparameters that are used based on the predicted values on predictive map264 or zones on predictive control zone map 265. The presentation can,in another example, include more abstracted information or more detailedinformation. The presentation can also include a confidence level thatindicates an accuracy with which the predictive values on predictive map264 or the zones on predictive control zone map 265 conform to measuredvalues that may be measured by sensors on agricultural harvester 100 asagricultural harvester 100 moves through the field. Further whereinformation is presented to more than one location, an authenticationand authorization system can be provided to implement authentication andauthorization processes. For instance, there may be a hierarchy ofindividuals that are authorized to view and change maps and otherpresented information. By way of example, an on-board display device mayshow the maps in near real time locally on the machine, or the maps mayalso be generated at one or more remote locations, or both. In someexamples, each physical display device at each location may beassociated with a person or a user permission level. The user permissionlevel may be used to determine which display markers are visible on thephysical display device and which values the corresponding person maychange. As an example, a local operator of agricultural harvester 100may be unable to see the information corresponding to the predictive map264 or make any changes to machine operation. A supervisor, such as asupervisor at a remote location, however, may be able to see thepredictive map 264 on the display but be prevented from making anychanges. A manager, who may be at a separate remote location, may beable to see all of the elements on predictive map 264 and also be ableto change the predictive map 264. In some instances, the predictive map264 accessible and changeable by a manager located remotely may be usedin machine control. This is one example of an authorization hierarchythat may be implemented. The predictive map 264 or predictive controlzone map 265 or both can be configured in other ways as well, asindicated by block 297.

At block 298, input from geographic position sensor 204 and otherin-situ sensors 208 are received by the control system. Block 300represents receipt by control system 214 of an input from the geographicposition sensor 204 identifying a geographic location of agriculturalharvester 100. Block 302 represents receipt by the control system 214 ofsensor inputs indicative of trajectory or heading of agriculturalharvester 100, and block 304 represents receipt by the control system214 of a speed of agricultural harvester 100. Block 306 representsreceipt by the control system 214 of other information from variousin-situ sensors 208.

At block 308, control system 214 generates control signals to controlthe controllable subsystems 216 based on the predictive map 264 orpredictive control zone map 265 or both and the input from thegeographic position sensor 204 and any other in-situ sensors 208. Atblock 310, control system 214 applies the control signals to thecontrollable subsystems. It will be appreciated that the particularcontrol signals that are generated, and the particular controllablesubsystems 216 that are controlled, may vary based upon one or moredifferent things. For example, the control signals that are generatedand the controllable subsystems 216 that are controlled may be based onthe type of predictive map 264 or predictive control zone map 265 orboth that is being used. Similarly, the control signals that aregenerated and the controllable subsystems 216 that are controlled andthe timing of the control signals can be based on various latencies ofcrop flow through the agricultural harvester 100 and the responsivenessof the controllable subsystems 216.

By way of example, a generated predictive map 264 in the form of apredictive biomass map can be used to control one or more controllablesubsystems 216. For instance, the predictive biomass map can includebiomass values georeferenced to locations within the field beingharvested. The biomass values from the predictive biomass map can beextracted and used to control the steering and propulsion subsystems 252and 250. By controlling the steering and propulsion subsystems 252 and250, a feed rate of material moving through the agricultural harvester100 can be controlled. Similarly, the header height can be controlled totake in more or less material, and, thus, the header height can also becontrolled to control feed rate of material through the agriculturalharvester 100. In other examples, if the predictive map 264 maps yieldrelative to positions in the field, control of agricultural harvester100 can be implemented. For example, if the values present in thepredictive yield map indicate a yield forward of agricultural harvester100 being higher on one portion of the header 102 than another portionof the header 102, control of header 102 may be implemented. Forexample, a draper speed on one side of header 102 may be increased ordecreased relative to the draper speed on the other side of header 102to account for the additional biomass. Thus, header and reel controller238 can be controlled using georeferenced values present in thepredictive yield map to control draper speeds of the draper belts onheader 102. Further, the header height can be changed automatically bythe header and reel controller 238 as the agricultural harvester 100proceeds through the field using georeferenced values obtained from thepredictive biomass map or the predictive yield map, as well as usinggeoreferenced values obtained from various other predictive maps. Thepreceding examples involving various controls using a predictive biomassmap or a predictive yield map are provided merely as examples.Consequently, a wide variety of other control signals can be generatedusing values obtained from a predictive biomass map, a predictive yieldmap, or other type of predictive map to control one or more of thecontrollable subsystems 216.

At block 312, a determination is made as to whether the harvestingoperation has been completed. If harvesting is not completed, theprocessing advances to block 314 where in-situ sensor data fromgeographic position sensor 204 and in-situ sensors 208 (and perhapsother sensors) continue to be read.

In some examples, at block 316, agricultural harvester 100 can alsodetect learning trigger criteria to perform machine learning on one ormore of the predictive map 264, predictive control zone map 265, themodel generated by predictive model generator 210, the zones generatedby control zone generator 213, one or more control algorithmsimplemented by the controllers in the control system 214, and othertriggered learning.

The learning trigger criteria can include any of a wide variety ofdifferent criteria. Some examples of detecting trigger criteria arediscussed with respect to blocks 318, 320, 321, 322 and 324. Forinstance, in some examples, triggered learning can involve recreation ofa relationship used to generate a predictive model when a thresholdamount of in-situ sensor data are obtained from in-situ sensors 208. Insuch examples, receipt of an amount of in-situ sensor data from thein-situ sensors 208 that exceeds a threshold triggers or causes thepredictive model generator 210 to generate a new predictive model thatis used by predictive map generator 212. Thus, as agricultural harvester100 continues a harvesting operation, receipt of the threshold amount ofin-situ sensor data from the in-situ sensors 208 triggers the creationof a new relationship represented by a predictive model generated bypredictive model generator 210. Further, new predictive map 264,predictive control zone map 265, or both can be regenerated using thenew predictive model. Block 318 represents detecting a threshold amountof in-situ sensor data used to trigger creation of a new predictivemodel.

In other examples, the learning trigger criteria may be based on howmuch the in-situ sensor data from the in-situ sensors 208 are changing,such as over time or compared to previous values. For example, ifvariations within the in-situ sensor data (or the relationship betweenthe in-situ sensor data and the information in prior information map258) are within a selected range or is less than a defined amount or isbelow a threshold value, then a new predictive model is not generated bythe predictive model generator 210. As a result, the predictive mapgenerator 212 does not generate a new predictive map 264, predictivecontrol zone map 265, or both. However, if variations within the in-situsensor data are outside of the selected range, are greater than thedefined amount, or are above the threshold value, for example, then thepredictive model generator 210 generates a new predictive model usingall or a portion of the newly received in-situ sensor data that thepredictive map generator 212 uses to generate a new predictive map 264.At block 320, variations in the in-situ sensor data, such as a magnitudeof an amount by which the data exceeds the selected range or a magnitudeof the variation of the relationship between the in-situ sensor data andthe information in the prior information map 258, can be used as atrigger to cause generation of a new predictive model and predictivemap. Keeping with the examples described above, the threshold, therange, and the defined amount can be set to default values; set by anoperator or user interaction through a user interface; set by anautomated system; or set in other ways.

Other learning trigger criteria can also be used. For instance, ifpredictive model generator 210 switches to a different prior informationmap (different from the originally selected prior information map 258),then switching to the different prior information map may triggerrelearning by predictive model generator 210, predictive map generator212, control zone generator 213, control system 214, or other items. Inanother example, transitioning of agricultural harvester 100 to adifferent topography or to a different control zone may be used aslearning trigger criteria as well.

In some instances, operator 260 can also edit the predictive map 264 orpredictive control zone map 265 or both. The edits can change a value onthe predictive map 264, change a size, shape, position, or existence ofa control zone on predictive control zone map 265, or both. Block 321shows that edited information can be used as learning trigger criteria.

In some instances, it may also be that operator 260 observes thatautomated control of a controllable subsystem, is not what the operatordesires. In such instances, the operator 260 may provide a manualadjustment to the controllable subsystem reflecting that the operator260 desires the controllable subsystem to operate in a different waythan is being commanded by control system 214. Thus, manual alterationof a setting by the operator 260 can cause one or more of predictivemodel generator 210 to relearn a model, predictive map generator 212 toregenerate map 264, control zone generator 213 to regenerate one or morecontrol zones on predictive control zone map 265, and control system 214to relearn a control algorithm or to perform machine learning on one ormore of the controller components 232 through 246 in control system 214based upon the adjustment by the operator 260, as shown in block 322.Block 324 represents the use of other triggered learning criteria.

In other examples, relearning may be performed periodically orintermittently based, for example, upon a selected time interval such asa discrete time interval or a variable time interval, as indicated byblock 326.

If relearning is triggered, whether based upon learning trigger criteriaor based upon passage of a time interval, as indicated by block 326,then one or more of the predictive model generator 210, predictive mapgenerator 212, control zone generator 213, and control system 214performs machine learning to generate a new predictive model, a newpredictive map, a new control zone, and a new control algorithm,respectively, based upon the learning trigger criteria. The newpredictive model, the new predictive map, and the new control algorithmare generated using any additional data that has been collected sincethe last learning operation was performed. Performing relearning isindicated by block 328.

If the harvesting operation has been completed, operation moves fromblock 312 to block 330 where one or more of the predictive map 264,predictive control zone map 265, and predictive model generated bypredictive model generator 210 are stored. The predictive map 264,predictive control zone map 265, and predictive model may be storedlocally on data store 202 or sent to a remote system using communicationsystem 206 for later use.

It will be noted that while some examples herein describe predictivemodel generator 210 and predictive map generator 212 receiving a priorinformation map in generating a predictive model and a functionalpredictive map, respectively, in other examples, the predictive modelgenerator 210 and predictive map generator 212 can receive, ingenerating a predictive model and a functional predictive map,respectively other types of maps, including predictive maps, such as afunctional predictive map generated during the harvesting operation.

FIG. 4A is a block diagram of a portion of the agricultural harvester100 shown in FIG. 1. Particularly, FIG. 4A shows, among other things,examples of the predictive model generator 210 and the predictive mapgenerator 212 in more detail. FIG. 4A also illustrates information flowamong the various components shown. The predictive model generator 210receives a seeding map 332 as a prior information map. Predictive modelgenerator 210 also receives a geographic location 334, or an indicationof a geographic location, from geographic position sensor 204. In-situsensors 208 illustratively include an agricultural characteristicsensor, such as agricultural characteristic sensor 336, as well as aprocessing system 338. In some instances, agricultural characteristicsensor 336 may be located on board the agricultural harvester 100.

Agricultural characteristics detected by the processing system 338 mayinclude any of a number of non-machine characteristics, such ascharacteristics of the field or characteristics of plants on the field(e.g., characteristics indicative of biomass or yield, crop statecharacteristics, such as downed crop data, crop size characteristics,such as crop height, crop stalk diameter, or ear size) as well as avariety of other non-machine characteristics of the environment in whichagricultural harvester 100 operates. Agricultural characteristicsdetected by the processing system 338 may also include any of a numberof machine characteristics of the agricultural harvester 100, or anothermachine, such as machine settings, machine performance characteristics,or machine operating characteristics (e.g., a height of header 102 fromthe field, a position or a speed of reel 164, a speed setting ofcleaning fan 120, a force required to drive threshing rotor 112, or aforward speed of agricultural harvester 100) as well as a variety ofother machine characteristics. Thus, in-situ sensors 208 may be anysensor that can detect an agricultural characteristic, such as anon-machine characteristic or a machine characteristic.

The processing system 338 processes sensor data generated fromagricultural characteristic sensor 336 to generate processed data, someexamples of which are described below. For example, agriculturalcharacteristic sensor 336 may be an optical sensor, such as a camera orother device that performs optical sensing. The optical sensor maygenerate images indicative of various agricultural characteristics, suchas non-machine characteristics or machine characteristics ofagricultural harvester 100, or another machine, as well as relatedcharacteristics. Processing system 338 processes one or more sensorsignals, such as images obtained from an optical sensor, to generateprocessed sensor data, such as processed image data, identifying one ormore non-machine characteristics, such as characteristics of the field,or processed sensor data identifying one or more characteristics ofagricultural harvester 100, such as machine settings, operatingcharacteristics, or characteristics of machine performance, or relatedcharacteristics.

Processing system 338 can also geolocate the values received from thein-situ sensor 208. For example, the location of the agriculturalharvester at the time a signal from in-situ sensor 208 is received istypically not the accurate location of the agricultural characteristicon the field. This is because an amount of time elapses between when theagricultural harvester makes initial contact with the agriculturalcharacteristic and when the agricultural characteristic is sensed byin-situ sensors 208. Thus, a transient time between when an agriculturalcharacteristic is initially encountered and when the agriculturalcharacteristic is sensed with the in-situ sensors 208 is taken intoaccount when georeferencing the sensed data. By doing so, the sensedcharacteristic can be accurately georeferenced to a location in thefield.

By way of example, an amount of time may elapse between when theagricultural harvester makes initial contact with a plant and when thecharacteristic of the plant is sensed. For instance, when detecting ayield characteristic based on sensing of processed grain that isdelivered to a storage location on the agricultural harvester, an amountof time may elapse between when the plant was encountered on the fieldand when the processed grain is sensed, such as in the storage location.Thus, a transient time between when a plant is initially encountered andwhen grain from the plant is sensed by an in-situ sensor 208 is takeninto account when georeferencing the sensed data. By doing so, the yieldcan be accurately georeferenced to a location on the field. Due totravel of severed crop along a header in a direction that is transverseto a direction of travel of the agricultural harvester 100, the yieldvalues normally geolocate to a chevron shaped area rearward of theagricultural harvester 100 as the agricultural harvester 100 travels ina forward direction. Processing system 338 allocates or apportions anaggregate yield detected by a yield sensor during each time ormeasurement interval back to earlier geo-referenced regions based uponthe travel times of the crop from different portions of the agriculturalharvester, such as different lateral locations along a width of a headerof the agricultural harvester. For example, processing system 338allocates a measured aggregate yield from a measurement interval or timeback to geo-referenced regions that were traversed by a header of theagricultural harvester during different measurement intervals or times.The processing system 338 apportions or allocates the aggregate yieldfrom a particular measurement interval or time to previously traversedgeo-referenced regions which are part of the chevron shape area.Similarly, in an example in which the in-situ sensor 208 is a threshingrotor drive force sensor that generates a sensor signal indicative ofbiomass, processing system 338 can geolocate the values, such as biomassvalues, by calculating a time delay between when the crop wasencountered on the field and when the crop will be threshed by threshingrotor 112. In such an example, the threshing rotor drive forcecharacteristic can be correlated, as an indicator of biomass, to thecorrect location on the field by taking into account the calculated timedelay. This time delay can be based on, at least in part, the forwardspeed of agricultural harvester 100. These are merely examples.

In some examples, characteristic sensor 336 can rely on wavelengths ofelectromagnetic energy and the way in which electromagnetic energy isreflected by, absorbed by, attenuated by, or transmitted through biomassor the harvested grain, for example. The agricultural characteristicsensor 336 may sense other electromagnetic properties of biomass orharvested grain, such as electrical permittivity, when the materialpasses between two capacitive plates. The agricultural characteristicsensor 336 may also rely on physical interaction associated with biomassor grains. For example, a signal can be produced by a piezoelectricsheet in response to an impact of biomass or grains there onto or asignal can be produced by a microphone or accelerometer in response to asound or vibration generated by an impact of biomass or grains ontoanother object. Other properties or interactions and sensors may also beused. In some examples, raw or processed data from agriculturalcharacteristic sensor 336 may be presented to operator 260 via operatorinterface mechanism 218. Operator 260 may be onboard the agriculturalharvester 100 or at a remote location.

The present discussion proceeds with respect to an example in whichagricultural characteristic sensor 336 is configured to senseagricultural characteristics, such as non-machine characteristics ormachine characteristics of the agricultural harvester 100 or anothermachine, or characteristics related respectively thereto. A non-machinecharacteristic, for the purpose of this disclosure, is any agriculturalcharacteristic that is not related to a machine. For instance,non-machine characteristics can include characteristics of the field onwhich agricultural harvester 100 operates, as well as various othernon-machine characteristics. It will be appreciated that the non-machinecharacteristic can be sensed externally of the agricultural harvester100 or internally within agricultural harvester 100. A machinecharacteristic, for the purpose of this disclosure, is any agriculturalcharacteristic which relates to a machine, such as agriculturalharvester 100 or another machine and includes, for example, machinesettings, operating characteristics, or characteristics of machineperformance, as well as other machine characteristics. It will be notedthat in some examples, a machine characteristic can also be indicativeof a non-machine characteristic, or vice versa. For instance, athreshing rotor drive force (a machine characteristic) can be indicativeof biomass (a non-machine characteristic).

It will be appreciated that these are just some examples, and thesensors mentioned above, as other examples of agriculturalcharacteristic sensor 336, are contemplated herein as well.Additionally, it will be appreciated that the in-situ sensors 208,including agricultural characteristic sensor 336, can sense any of anumber of agricultural characteristics. The predictive model generator210, discussed below, can identify a relationship between one or moreagricultural characteristics detected or represented in sensor data, ata geographic location corresponding to the sensor data, and one or moreseeding characteristic values from a seeding map, such as seeding map332, corresponding to the same location in the field. On the basis ofthat relationship, the predictive model generator 210 generates apredictive agricultural characteristic model. Further, it will beappreciated that predictive map generator 212, discussed below, can usethe characteristic models generated by predictive model generator 210 togenerate a functional predictive map, such as a functional predictiveagricultural characteristic map. The generated functional predictive mappredicts one or more agricultural characteristics at different locationsin the field based upon georeferenced seeding characteristic valuescontained in the seeding map 332 at the same locations in the field.

As shown in FIG. 4A, the example predictive model generator 210 includesone or more of a non-machine characteristic-to-population modelgenerator 342, a non-machine characteristic-to-genotype model generator344, a machine characteristic-to-population model generator 346, and amachine characteristic-to-genotype model generator 347. In otherexamples, the predictive model generator 210 may include additional,fewer, or different components than those shown in the example of FIG.4A. Consequently, in some examples, the predictive model generator 210also may include other items 348. Other items 348 may include othertypes of predictive model generators to generate other types ofagricultural characteristic models. For instance, other items 348 mayinclude other non-machine characteristics models or other machinecharacteristics models, such as a non-machine characteristic-to-otherseeding characteristic model or a machine characteristic-to-otherseeding characteristic model. Other seeding characteristics can include,for instance, location (e.g., geographic location of the seeds in thefield); spacing (e.g., spacing between individual seeds and spacingbetween seed rows); population, which can be derived from spacing;orientation (e.g., seed orientation in a trench and orientation of seedrows); depth (e.g., seed depth and furrow depth); dimensions (e.g., seedsize); and genotype (e.g., seed species, seed hybrid, seed variety, seedcultivar, etc.). Other seeding characteristics may also be included,such as various characteristics of the seedbed or seed trench.

Non-machine characteristic-to-population model generator 342 identifiesa relationship between a non-machine characteristic detected orrepresented in sensor data 340, at a geographic location correspondingto the sensor data 340, and plant population values from the seeding map332 corresponding to the same location in the field where theenvironmental characteristic was detected or corresponds. Based on thisrelationship established by environmental characteristic-to-populationmodel generator 342, environmental characteristic-to-population modelgenerator 342 generates a predictive agricultural characteristic model.The predictive agricultural characteristic model is used by non-machinecharacteristic map generator 352 to predict the non-machinecharacteristic at different locations in the field based upon thegeoreferenced plant population value contained in the seeding map 332 atthe same locations in the field.

Non-machine characteristic-to-genotype model generator 344 identifies arelationship between a non-machine characteristic detected orrepresented in sensor data 340, at a geographic location correspondingto the sensor data 340, and the genotype values from the seeding map 332corresponding to the same location in the field where the non-machinecharacteristic was detected or corresponds. Based on this relationshipestablished by non-machine characteristic-to-genotype model generator344, non-machine characteristic-to-genotype model generator 344generates a predictive agricultural characteristic model. The predictiveagricultural characteristic model is used by non-machine characteristicmap generator 352 to predict the non-machine characteristic at differentlocations in the field based upon the georeferenced genotype valuecontained in the seeding map 332 at the same locations in the field.

Machine characteristic-to-population model generator 346 identifies arelationship between a machine characteristic detected or represented insensor data 340, at a geographic location corresponding to the sensordata, and plant population values from the seeding map 332 correspondingto the same location in the field where the machine characteristic wasdetected or corresponds. Based on this relationship established bymachine characteristic-to-population model generator 346, machinecharacteristic-to-population model generator 346 generates a predictiveagricultural characteristic model. The predictive agriculturalcharacteristic model is used by machine characteristic map generator 354to predict the machine characteristic at different locations in thefield based upon the georeferenced plant population value contained inthe seeding map 332 at the same locations in the field.

Machine characteristics-to-genotype model generator 347 identifies arelationship between a machine characteristic detected or represented insensor data 340, at a geographic location corresponding to the sensordata, and the genotype value from the seeding map 332 corresponding tothe same location in the field where the machine characteristic wasdetected or corresponds. Based on this relationship established bymachine characteristic-to-genotype model generator 347, machinecharacteristic-to-genotype model generator 347 generates a predictiveagricultural characteristic model. The predictive agriculturalcharacteristic model is used by machine characteristic map generator 354to predict the machine characteristic at different locations in thefield based upon the georeferenced genotype value contained in theseeding map at the same locations in the field.

In light of the above, the predictive model generator 210 is operable toproduce a plurality of predictive agricultural characteristic models,such as one or more of the predictive agricultural characteristic modelsgenerated by model generators 342, 344, 346, 347, or 348. In anotherexample, two or more of the predictive agricultural characteristicmodels described above may be combined into a single predictiveagricultural characteristic model that predicts two or more ofnon-machine characteristics or machine characteristics based upon theseeding characteristic values at different locations in the field. Anyof these agricultural characteristic models, or combinations thereof,are represented collectively by characteristic model 350 in FIG. 4A.

The predictive agricultural characteristic model 350 is provided topredictive map generator 212. In the example of FIG. 4A, predictive mapgenerator 212 includes a non-machine characteristic map generator 352and a machine characteristic map generator 354. In other examples, thepredictive map generator 212 may include additional, fewer, or differentmap generators. Thus, in some examples, the predictive map generator 212may include other items 358 which may include other types of mapgenerators to generate characteristic maps for other types ofcharacteristics. Non-machine characteristic map generator 352 receivesthe seeding map 332 and the predictive agricultural characteristic model350 (which predicts non-machine characteristics based upon a seedingcharacteristic value in the seeding map 332), and generates a predictivemap that predicts the non-machine characteristic at different locationsin the field.

Machine characteristic map generator 354 receives the seeding map 332and the predictive characteristic model 350 (which predicts machinecharacteristics based upon a seeding characteristic value in the seedingmap 332), and generates a predictive map that predicts the machinecharacteristic at different locations in the field.

Predictive map generator 212 outputs one or more functional predictiveagricultural characteristic maps 360 that are predictive of one or moreof non-machine characteristics or machine characteristics. Each of thefunctional predictive agricultural characteristic maps 360 predicts therespective agricultural characteristic at different locations in afield. Each of the generated functional predictive agriculturalcharacteristic maps 360 may be provided to control zone generator 213,control system 214, or both, as shown in FIG. 2. Control zone generator213 generates control zones and incorporates those control zones intothe functional predictive map, i.e., predictive map 360, to produce afunctional predictive map 360 with control zones. The functionalpredictive map 360 (with or without control zones) may be provided tocontrol system 214, which generates control signals to control one ormore of the controllable subsystems 216 based upon the functionalpredictive map 360 (with or without control zones).

FIG. 4B is a block diagram showing some examples of real-time (in-situ)sensors 208. Some of the sensors shown in FIG. 4B, or differentcombinations of them, may have both a sensor 336 and a processing system338. Some of the possible in-situ sensors 208 shown in FIG. 4B are shownand described above with respect to previous FIGS. and are similarlynumbered. FIG. 4B shows that in-situ sensors 208 can include operatorinput sensors 980, machine sensors 982, harvested material propertysensors 984, field and soil property sensors 985, environmentalcharacteristic sensors 987, and they may include a wide variety of othersensors 226. Non-machine sensors 983 include, operator input sensor(s)980, harvested material property sensor(s) 984, field and soil propertysensor(s) 985, environmental characteristic sensor(s) 987 and caninclude other sensors 226 as well. Operator input sensors 980 may besensors that sense operator inputs through operator interface mechanisms218. Therefore, operator input sensors 980 may sense user movement oflinkages, joysticks, a steering wheel, buttons, dials, or pedals.Operator input sensors 980 can also sense user interactions with otheroperator input mechanisms, such as with a touch sensitive screen, with amicrophone where speech recognition is utilized, or any of a widevariety of other operator input mechanisms.

Machine sensors 982 may sense different characteristics of agriculturalharvester 100. For instance, as discussed above, machine sensors 982 mayinclude machine speed sensors 146, separator loss sensor 148, cleangrain camera 150, forward looking image capture mechanism 151, losssensors 152 or geographic position sensor 204, examples of which aredescribed above. Machine sensors 982 can also include machine settingsensors 991 that sense machine settings. Some examples of machinesettings were described above with respect to FIG. 1. Front-endequipment (e.g., header) position sensor 993 can sense the position ofthe header 102, reel 164, cutter 104, or other front-end equipmentrelative to the frame of agricultural harvester 100. For instance,sensors 993 may sense the height of header 102 above the ground. Machinesensors 982 can also include front-end equipment (e.g., header)orientation sensors 995. Sensors 995 may sense the orientation of header102 relative to agricultural harvester 100, or relative to the ground.Machine sensors 982 may include stability sensors 997. Stability sensors997 sense oscillation or bouncing motion (and amplitude) of agriculturalharvester 100. Machine sensors 982 may also include residue settingsensors 999 that are configured to sense whether agricultural harvester100 is configured to chop the residue, produce a windrow, or deal withthe residue in another way. Machine sensors 982 may include cleaningshoe fan speed sensor 951 that senses the speed of cleaning fan 120.Machine sensors 982 may include concave clearance sensors 953 that sensethe clearance between the rotor 112 and concaves 114 on agriculturalharvester 100. Machine sensors 982 may include chaffer clearance sensors955 that sense the size of openings in chaffer 122. The machine sensors982 may include threshing rotor speed sensor 957 that senses a rotorspeed of rotor 112. Machine sensors 982 may include rotor pressuresensor 959 that senses the pressure used to drive rotor 112. Machinesensors 982 may include sieve clearance sensor 961 that senses the sizeof openings in sieve 124. The machine sensors 982 may include MOGmoisture sensor 963 that senses a moisture level of the MOG passingthrough agricultural harvester 100. Machine sensors 982 may includemachine orientation sensor 965 that senses the orientation ofagricultural harvester 100. Machine sensors 982 may include materialfeed rate sensors 967 that sense the feed rate of material as thematerial travels through feeder house 106, clean grain elevator 130, orelsewhere in agricultural harvester 100. Machine sensors 982 can includebiomass sensors 969 that sense the biomass traveling through feederhouse 106, through separator 116, or elsewhere in agricultural harvester100. The machine sensors 982 may include fuel consumption sensor 971that senses a rate of fuel consumption over time of agriculturalharvester 100. Machine sensors 982 may include power utilization sensor973 that senses power utilization in agricultural harvester 100, such aswhich subsystems are utilizing power, or the rate at which subsystemsare utilizing power, or the distribution of power among the subsystemsin agricultural harvester 100. Machine sensors 982 may include tirepressure sensors 977 that sense the inflation pressure in tires 144 ofagricultural harvester 100. Machine sensor 982 may include a widevariety of other machine performance sensors, or machine characteristicsensors, indicated by block 975. The machine performance sensors andmachine characteristic sensors 975 may sense machine performance orcharacteristics of agricultural harvester 100.

Harvested material property sensors 984 may sense characteristics of thesevered crop material as the crop material is being processed byagricultural harvester 100. The crop properties may include such thingsas crop type, crop moisture, grain quality (such as broken grain), MOGlevels, grain constituents such as starches and protein, MOG moisture,and other crop material properties. Other sensors could sense straw“toughness”, adhesion of corn to ears, and other characteristics thatmight be beneficially used to control processing for better graincapture, reduced grain damage, reduced power consumption, reduced grainloss, etc.

Field and soil property sensors 985 may sense characteristics of thefield and soil. The field and soil properties may include soil moisture,soil compactness, the presence and location of standing water, soiltype, and other soil and field characteristics.

Environmental characteristic sensors 987 may sense one or moreenvironmental characteristics. The environmental characteristics mayinclude such things as wind direction and wind speed, precipitation,fog, dust level or other obscurants, or other environmentalcharacteristics.

In some examples, one or more of the sensors shown in FIG. 4B areprocessed to receive processed data 340 and used inputs to modelgenerator 210. Model generator 210 generates a model indicative of therelationship between the sensor data and one or more of the prior orpredictive information maps. The model is provided to map generator 212that generates a map that maps predictive sensor data valuescorresponding to the sensor from FIG. 4B or a related characteristic.

FIG. 5 is a flow diagram of an example of operation of predictive modelgenerator 210 and predictive map generator 212 in generating thepredictive agricultural characteristic model 350 and the functionalpredictive agricultural characteristic map 360, respectively. At block362, predictive model generator 210 and predictive map generator 212receive a prior seeding map 332. At block 364, processing system 338receives one or more sensor signals from in-situ sensors 208, such asagricultural characteristic sensor 336. As discussed above, theagricultural characteristic sensor 336 may be a non-machinecharacteristic sensor, as indicated by block 366; a machinecharacteristic sensor, as indicated by block 368; or another type ofagricultural characteristic sensor, as indicated by block 370.

At block 372, processing system 338 processes the one or more receivedsensor signals to generate sensor data indicative of an agriculturalcharacteristic present in the one or more sensor signals or of a relatedcharacteristic. At block 374, the sensor data may be indicative of oneor more non-machine characteristics that exist at or correspond to alocation on the field, such as at a location in front of a combineharvester. In some instances, as indicated at block 376, the sensor datamay be indicative of one or more machine characteristics that exist ator correspond to a location on the field. In some instances, asindicated at block 380, the sensor data may be indicative of anotheragricultural characteristic.

At block 382, predictive model generator 210 also obtains the geographiclocation corresponding to the sensor data. For instance, the predictivemodel generator 210 can obtain the geographic position, or an indicationof the geographic position, from geographic position sensor 204 anddetermine, based upon machine delays, machine speed, etc., a precisegeographic location on the field to which the sensor data corresponds,such as a precise geographic location where the sensor signal wasgenerated or from which the sensor data 340 was derived.

At block 384, predictive model generator 210 generates one or morepredictive models, such as predictive agricultural characteristic model350, that model a relationship between a seeding characteristic valueobtained from seeding map 332, and a characteristic being sensed by thein-situ sensor 208 or a related characteristic. For instance, predictivemodel generator 210 may generate a predictive agriculturalcharacteristic model that models a relationship between a seedingcharacteristic value and a sensed agricultural characteristic, such as anon-machine characteristic or a machine characteristic indicated by thesensor data obtained from in-situ sensor 208 or a relatedcharacteristic.

At block 386, the predictive model, such as predictive agriculturalcharacteristic model 350, is provided to predictive map generator 212,which generates a functional predictive map, such as functionalpredictive agricultural characteristic map 360 that maps a predictedagricultural characteristic based on the seeding map, or thegeoreferenced seeding characteristic values therein, and the predictiveagricultural characteristic model 350. In some examples, the functionalpredictive agricultural characteristic map 360 predicts a non-machinecharacteristic, as indicated by block 388. In some examples, thefunctional predictive agricultural characteristic map 360 predicts amachine characteristic, as indicated by block 390. In still otherexamples, the functional predictive agricultural characteristic map 360predicts other items, as indicated by block 392. For instance, in otherexamples, the functional predictive agricultural characteristic map 360may predict one or more machine characteristics along with one or morenon-machine characteristics, or vice versa. Further, the functionalpredictive agricultural characteristic map 360 can be generated duringthe course of an agricultural operation. Thus, as an agriculturalharvester is moving through a field performing an agriculturaloperation, the functional predictive agricultural characteristic map 360is generated as the agricultural operation is being performed.

At block 394, predictive map generator 212 outputs the functionalpredictive agricultural characteristic map 360. At block 391 predictivemap generator 212 outputs the functional predictive agriculturalcharacteristic map for presentation to and possible interaction byoperator 260. At block 393, predictive map generator 212 may configurethe functional predictive agricultural characteristic map forconsumption by control system 214. At block 395, predictive mapgenerator 212 can also provide the functional predictive agriculturalcharacteristic map 360 to control zone generator 213 for generation andincorporation of control zones. At block 397, predictive map generator212 configures the functional predictive agricultural characteristic map360 in other ways as well. The functional predictive agriculturalcharacteristic map 360 (with or without the control zones) is providedto control system 214. At block 396, control system 214 generatescontrol signals to control the controllable subsystems 216 based uponthe predictive characteristic map 360 (with or without control zones).

Control system 214 can generate control signals to control header orother machine actuator(s) 248. Control system 214 can generate controlsignals to control propulsion subsystem 250. Control system 214 cangenerate control signals to control steering subsystem 252. Controlsystem 214 can generate control signals to control residue subsystem138. Control system 214 can generate control signals to control machinecleaning subsystem 254. Control system 214 can generate control signalsto control thresher 110. Control system 214 can generate control signalsto control material handling subsystem 125. Control system 214 cangenerate control signals to control crop cleaning subsystem 118. Controlsystem 214 can generate control signals to control communication system206. Control system 214 can generate control signals to control operatorinterface mechanisms 218. Control system 214 can generate controlsignals to control various other controllable subsystems 256.

It can thus be seen that the present system takes a prior informationmap that maps a characteristic, such as a seeding characteristic valueor information from a prior operation pass, to different locations in afield. The present system also uses one or more in-situ sensors thatsense in-situ sensor data that is indicative of an agriculturalcharacteristic, such as a non-machine characteristic, a machinecharacteristic, or another agricultural characteristic capable of beingsensed by in-situ sensors or indicated by characteristics sensed byin-situ sensors and generates a model that models a relationship betweenthe agricultural characteristic sensed using the in-situ sensor, or arelated characteristic, and the characteristic mapped in the priorinformation map. Thus, the present system generates a functionalpredictive map using a model, in-situ data, and a prior information mapand may configure the generated functional predictive map forconsumption by a control system, for presentation to a local or remoteoperator or other user, or both. For example, the control system may usethe map to control one or more systems of an agricultural harvester.

The present discussion has mentioned processors and servers. In someexamples, the processors and servers include computer processors withassociated memory and timing circuitry, not separately shown. Theprocessors and servers are functional parts of the systems or devices towhich the processors and servers belong and are activated by andfacilitate the functionality of the other components or items in thosesystems.

Also, a number of user interface displays have been discussed. Thedisplays can take a wide variety of different forms and can have a widevariety of different user actuatable operator interface mechanismsdisposed thereon. For instance, user actuatable operator interfacemechanisms may include text boxes, check boxes, icons, links, drop-downmenus, search boxes, etc. The user actuatable operator interfacemechanisms can also be actuated in a wide variety of different ways. Forinstance, the user actuatable operator interface mechanisms can beactuated using operator interface mechanisms such as a point and clickdevice, such as a track ball or mouse, hardware buttons, switches, ajoystick or keyboard, thumb switches or thumb pads, etc., a virtualkeyboard or other virtual actuators. In addition, where the screen onwhich the user actuatable operator interface mechanisms are displayed isa touch sensitive screen, the user actuatable operator interfacemechanisms can be actuated using touch gestures. Also, user actuatableoperator interface mechanisms can be actuated using speech commandsusing speech recognition functionality. Speech recognition may beimplemented using a speech detection device, such as a microphone, andsoftware that functions to recognize detected speech and executecommands based on the received speech.

A number of data stores have also been discussed. It will be noted thedata stores can each be broken into multiple data stores. In someexamples, one or more of the data stores may be local to the systemsaccessing the data stores, one or more of the data stores may all belocated remote form a system utilizing the data store, or one or moredata stores may be local while others are remote. All of theseconfigurations are contemplated by the present disclosure.

Also, the figures show a number of blocks with functionality ascribed toeach block. It will be noted that fewer blocks can be used to illustratethat the functionality ascribed to multiple different blocks isperformed by fewer components. Also, more blocks can be usedillustrating that the functionality may be distributed among morecomponents. In different examples, some functionality may be added, andsome may be removed.

It will be noted that the above discussion has described a variety ofdifferent systems, components, logic, and interactions. It will beappreciated that any or all of such systems, components, logic andinteractions may be implemented by hardware items, such as processors,memory, or other processing components, some of which are describedbelow, that perform the functions associated with those systems,components, logic, or interactions. In addition, any or all of thesystems, components, logic and interactions may be implemented bysoftware that is loaded into a memory and is subsequently executed by aprocessor or server or other computing component, as described below.Any or all of the systems, components, logic and interactions may alsobe implemented by different combinations of hardware, software,firmware, etc., some examples of which are described below. These aresome examples of different structures that may be used to implement anyor all of the systems, components, logic and interactions describedabove. Other structures may be used as well.

FIG. 6 is a block diagram of agricultural harvester 600, which may besimilar to agricultural harvester 100 shown in FIG. 2. The agriculturalharvester 600 communicates with elements in a remote server architecture500. In some examples, remote server architecture 500 providescomputation, software, data access, and storage services that do notrequire end-user knowledge of the physical location or configuration ofthe system that delivers the services. In various examples, remoteservers may deliver the services over a wide area network, such as theinternet, using appropriate protocols. For instance, remote servers maydeliver applications over a wide area network and may be accessiblethrough a web browser or any other computing component. Software orcomponents shown in FIG. 2 as well as data associated therewith, may bestored on servers at a remote location. The computing resources in aremote server environment may be consolidated at a remote data centerlocation, or the computing resources may be dispersed to a plurality ofremote data centers. Remote server infrastructures may deliver servicesthrough shared data centers, even though the services appear as a singlepoint of access for the user. Thus, the components and functionsdescribed herein may be provided from a remote server at a remotelocation using a remote server architecture. Alternatively, thecomponents and functions may be provided from a server, or thecomponents and functions can be installed on client devices directly, orin other ways.

In the example shown in FIG. 6, some items are similar to those shown inFIG. 2 and those items are similarly numbered. FIG. 6 specifically showsthat predictive model generator 210 or predictive map generator 212, orboth, may be located at a server location 502 that is remote from theagricultural harvester 600. Therefore, in the example shown in FIG. 6,agricultural harvester 600 accesses systems through remote serverlocation 502.

FIG. 6 also depicts another example of a remote server architecture.FIG. 6 shows that some elements of FIG. 2 may be disposed at a remoteserver location 502 while others may be located elsewhere. By way ofexample, data store 202 may be disposed at a location separate fromlocation 502 and accessed via the remote server at location 502.Regardless of where the elements are located, the elements can beaccessed directly by agricultural harvester 600 through a network suchas a wide area network or a local area network; the elements can behosted at a remote site by a service; or the elements can be provided asa service or accessed by a connection service that resides in a remotelocation. Also, data may be stored in any location, and the stored datamay be accessed by, or forwarded to, operators, users, or systems. Forinstance, physical carriers may be used instead of, or in addition to,electromagnetic wave carriers. In some examples, where wirelesstelecommunication service coverage is poor or nonexistent, anothermachine, such as a fuel truck or other mobile machine or vehicle, mayhave an automated, semi-automated, or manual information collectionsystem. As the combine harvester 600 comes close to the machinecontaining the information collection system, such as a fuel truck priorto fueling, the information collection system collects the informationfrom the combine harvester 600 using any type of ad-hoc wirelessconnection. The collected information may then be forwarded to anothernetwork when the machine containing the received information reaches alocation where wireless telecommunication service coverage or otherwireless coverage is available. For instance, a fuel truck may enter anarea having wireless communication coverage when traveling to a locationto fuel other machines or when at a main fuel storage location. All ofthese architectures are contemplated herein. Further, the informationmay be stored on the agricultural harvester 600 until the agriculturalharvester 600 enters an area having wireless communication coverage. Theagricultural harvester 600, itself, may send the information to anothernetwork.

It will also be noted that the elements of FIG. 2, or portions thereof,may be disposed on a wide variety of different devices. One or more ofthose devices may include an on-board computer, an electronic controlunit, a display unit, a server, a desktop computer, a laptop computer, atablet computer, or other mobile device, such as a palm top computer, acell phone, a smart phone, a multimedia player, a personal digitalassistant, etc.

In some examples, remote server architecture 500 may includecybersecurity measures. Without limitation, these measures may includeencryption of data on storage devices, encryption of data sent betweennetwork nodes, authentication of people or processes accessing data, aswell as the use of ledgers for recording metadata, data, data transfers,data accesses, and data transformations. In some examples, the ledgersmay be distributed and immutable (e.g., implemented as blockchain).

FIG. 7 is a simplified block diagram of one illustrative example of ahandheld or mobile computing device that can be used as a user's orclient's hand held device 16, in which the present system (or parts ofit) can be deployed. For instance, a mobile device can be deployed inthe operator compartment of agricultural harvester 100 for use ingenerating, processing, or displaying the maps discussed above. FIGS.8-9 are examples of handheld or mobile devices.

FIG. 7 provides a general block diagram of the components of a clientdevice 16 that can run some components shown in FIG. 2, that interactswith them, or both. In the device 16, a communications link 13 isprovided that allows the handheld device to communicate with othercomputing devices and under some examples provides a channel forreceiving information automatically, such as by scanning. Examples ofcommunications link 13 include allowing communication though one or morecommunication protocols, such as wireless services used to providecellular access to a network, as well as protocols that provide localwireless connections to networks.

In other examples, applications can be received on a removable SecureDigital (SD) card that is connected to an interface 15. Interface 15 andcommunication links 13 communicate with a processor 17 (which can alsoembody processors or servers from other FIGS.) along a bus 19 that isalso connected to memory 21 and input/output (I/O) components 23, aswell as clock 25 and location system 27.

I/O components 23, in one example, are provided to facilitate input andoutput operations. I/O components 23 for various examples of the device16 can include input components such as buttons, touch sensors, opticalsensors, microphones, touch screens, proximity sensors, accelerometers,orientation sensors and output components such as a display device, aspeaker, and or a printer port. Other I/O components 23 can be used aswell.

Clock 25 illustratively comprises a real time clock component thatoutputs a time and date. It can also, illustratively, provide timingfunctions for processor 17.

Location system 27 illustratively includes a component that outputs acurrent geographical location of device 16. This can include, forinstance, a global positioning system (GPS) receiver, a LORAN system, adead reckoning system, a cellular triangulation system, or otherpositioning system. Location system 27 can also include, for example,mapping software or navigation software that generates desired maps,navigation routes and other geographic functions.

Memory 21 stores operating system 29, network settings 31, applications33, application configuration settings 35, data store 37, communicationdrivers 39, and communication configuration settings 41. Memory 21 caninclude all types of tangible volatile and non-volatilecomputer-readable memory devices. Memory 21 may also include computerstorage media (described below). Memory 21 stores computer readableinstructions that, when executed by processor 17, cause the processor toperform computer-implemented steps or functions according to theinstructions. Processor 17 may be activated by other components tofacilitate their functionality as well.

FIG. 8 shows one example in which device 16 is a tablet computer 600. InFIG. 8, computer 600 is shown with user interface display screen 602.Screen 602 can be a touch screen or a pen-enabled interface thatreceives inputs from a pen or stylus. Tablet computer 600 may also usean on-screen virtual keyboard. Of course, computer 600 might also beattached to a keyboard or other user input device through a suitableattachment mechanism, such as a wireless link or USB port, for instance.Computer 600 may also illustratively receive voice inputs as well.

FIG. 9 is similar to FIG. 8 except that the device is a smart phone 71.Smart phone 71 has a touch sensitive display 73 that displays icons ortiles or other user input mechanisms 75. Mechanisms 75 can be used by auser to run applications, make calls, perform data transfer operations,etc. In general, smart phone 71 is built on a mobile operating systemand offers more advanced computing capability and connectivity than afeature phone.

Note that other forms of the devices 16 are possible.

FIG. 10 is one example of a computing environment in which elements ofFIG. 2 can be deployed. With reference to FIG. 10, an example system forimplementing some embodiments includes a computing device in the form ofa computer 810 programmed to operate as discussed above. Components ofcomputer 810 may include, but are not limited to, a processing unit 820(which can comprise processors or servers from previous FIGS.), a systemmemory 830, and a system bus 821 that couples various system componentsincluding the system memory to the processing unit 820. The system bus821 may be any of several types of bus structures including a memory busor memory controller, a peripheral bus, and a local bus using any of avariety of bus architectures. Memory and programs described with respectto FIG. 2 can be deployed in corresponding portions of FIG. 10.

Computer 810 typically includes a variety of computer readable media.Computer readable media may be any available media that can be accessedby computer 810 and includes both volatile and nonvolatile media,removable and non-removable media. By way of example, and notlimitation, computer readable media may comprise computer storage mediaand communication media. Computer storage media is different from, anddoes not include, a modulated data signal or carrier wave. Computerreadable media includes hardware storage media including both volatileand nonvolatile, removable and non-removable media implemented in anymethod or technology for storage of information such as computerreadable instructions, data structures, program modules or other data.Computer storage media includes, but is not limited to, RAM, ROM,EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by computer 810. Communication media mayembody computer readable instructions, data structures, program modulesor other data in a transport mechanism and includes any informationdelivery media. The term “modulated data signal” means a signal that hasone or more of its characteristics set or changed in such a manner as toencode information in the signal.

The system memory 830 includes computer storage media in the form ofvolatile and/or nonvolatile memory or both such as read only memory(ROM) 831 and random access memory (RAM) 832. A basic input/outputsystem 833 (BIOS), containing the basic routines that help to transferinformation between elements within computer 810, such as duringstart-up, is typically stored in ROM 831. RAM 832 typically containsdata or program modules or both that are immediately accessible toand/or presently being operated on by processing unit 820. By way ofexample, and not limitation, FIG. 10 illustrates operating system 834,application programs 835, other program modules 836, and program data837.

The computer 810 may also include other removable/non-removablevolatile/nonvolatile computer storage media. By way of example only,FIG. 10 illustrates a hard disk drive 841 that reads from or writes tonon-removable, nonvolatile magnetic media, an optical disk drive 855,and nonvolatile optical disk 856. The hard disk drive 841 is typicallyconnected to the system bus 821 through a non-removable memory interfacesuch as interface 840, and optical disk drive 855 are typicallyconnected to the system bus 821 by a removable memory interface, such asinterface 850.

Alternatively, or in addition, the functionality described herein can beperformed, at least in part, by one or more hardware logic components.For example, and without limitation, illustrative types of hardwarelogic components that can be used include Field-programmable Gate Arrays(FPGAs), Application-specific Integrated Circuits (e.g., ASICs),Application-specific Standard Products (e.g., ASSPs), System-on-a-chipsystems (SOCs), Complex Programmable Logic Devices (CPLDs), etc.

The drives and their associated computer storage media discussed aboveand illustrated in FIG. 10, provide storage of computer readableinstructions, data structures, program modules and other data for thecomputer 810. In FIG. 10, for example, hard disk drive 841 isillustrated as storing operating system 844, application programs 845,other program modules 846, and program data 847. Note that thesecomponents can either be the same as or different from operating system834, application programs 835, other program modules 836, and programdata 837.

A user may enter commands and information into the computer 810 throughinput devices such as a keyboard 862, a microphone 863, and a pointingdevice 861, such as a mouse, trackball or touch pad. Other input devices(not shown) may include a joystick, game pad, satellite dish, scanner,or the like. These and other input devices are often connected to theprocessing unit 820 through a user input interface 860 that is coupledto the system bus, but may be connected by other interface and busstructures. A visual display 891 or other type of display device is alsoconnected to the system bus 821 via an interface, such as a videointerface 890. In addition to the monitor, computers may also includeother peripheral output devices such as speakers 897 and printer 896,which may be connected through an output peripheral interface 895.

The computer 810 is operated in a networked environment using logicalconnections (such as a controller area network—CAN, local areanetwork—LAN, or wide area network WAN) to one or more remote computers,such as a remote computer 880.

When used in a LAN networking environment, the computer 810 is connectedto the LAN 871 through a network interface or adapter 870. When used ina WAN networking environment, the computer 810 typically includes amodem 872 or other means for establishing communications over the WAN873, such as the Internet. In a networked environment, program modulesmay be stored in a remote memory storage device. FIG. 10 illustrates,for example, that remote application programs 885 can reside on remotecomputer 880.

It should also be noted that the different examples described herein canbe combined in different ways. That is, parts of one or more examplescan be combined with parts of one or more other examples. All of this iscontemplated herein.

Example 1 is an agricultural work machine, comprising:

a communication system that receives a prior information map thatincludes values of a seeding characteristic corresponding to differentgeographic locations in a field;

a geographic position sensor that detects a geographic location of theagricultural work machine;

an in-situ sensor that detects a value of an agricultural characteristiccorresponding to the geographic location;

a predictive model generator that generates a predictive agriculturalmodel that models a relationship between the seeding characteristic andthe agricultural characteristic based on a value of the seedingcharacteristic in the prior information map at the geographic locationand the value of the agricultural characteristic detected by the in-situsensor corresponding to the geographic location.

a predictive map generator that generates a functional predictiveagricultural characteristic map of the field that maps predictive valuesof the agricultural characteristic to the different geographic locationsin the field based on the values of the seeding characteristic in theprior information map and based on the predictive agricultural model.

Example 2 is the agricultural work machine of any or all previousexamples, wherein the predictive map generator configures the functionalpredictive agricultural characteristic map for consumption by a controlsystem that generates control signals to control a controllablesubsystem on the agricultural work machine based on the functionalpredictive agricultural characteristic map.

Example 3 is the agricultural work machine of any or all previousexamples, wherein the in-situ sensor comprises:

an optical sensor configured to detect an image indicative of theagricultural characteristic.

Example 4 is the agricultural work machine of any or all previousexamples, wherein the optical sensor is oriented to detect an image ofat least a portion of the field and further comprises:

an image processing system configured to process the image to identifythe value of the agricultural characteristic in the image indicative ofthe agricultural characteristic.

Example 5 is the agricultural work machine of any or all previousexamples, wherein the in-situ sensor on the agricultural work machine isconfigured to detect, as the value of the agricultural characteristic, avalue of a non-machine characteristic corresponding to the geographiclocation.

Example 6 is the agricultural work machine of any or all previousexamples, wherein the prior information map includes, as the values ofthe seeding characteristic, genotype values corresponding to thedifferent geographic locations in the field, and wherein the predictivemodel generator is configured to identify a relationship between thegenotype values and the non-machine characteristic based on the value ofthe non-machine characteristic corresponding to the geographic locationand the genotype value, in the prior information map, at the geographiclocation, the predictive characteristic model being configured toreceive a genotype value as a model input and generate a predictivevalue of the non-machine characteristic as a model output based on theidentified relationship.

Example 7 is the agricultural work machine of any or all previousexamples, wherein the prior information map includes, as the values ofthe seeding characteristic, population values corresponding to thedifferent geographic locations in the field, and wherein the predictivemodel generator is configured to identify a relationship between thepopulation values and the non-machine characteristic based on the valueof the non-machine characteristic corresponding to the geographiclocation and the population value, in the prior information map, at thegeographic location, the predictive characteristic model beingconfigured to receive a population value as a model input and generate apredictive value of the non-machine characteristic as a model outputbased on the identified relationship.

Example 8 is the agricultural work machine of any or all previousexamples, wherein the in-situ sensor on the agricultural work machine isconfigured to detect, as the value of the agricultural characteristic, avalue of a machine characteristic corresponding to the geographiclocation.

Example 9 is the agricultural work machine of any or all previousexamples, wherein the prior information map includes, as the values ofthe seeding characteristic, genotype values corresponding to thedifferent geographic locations in the field, and wherein the predictivemodel generator is configured to identify a relationship between thegenotype values and the machine characteristic based on the value of themachine characteristic corresponding to the geographic location and thegenotype value, in the prior information map, at the geographiclocation, the predictive characteristic model being configured toreceive a genotype value as a model input and generate a predictivevalue of the machine characteristic as a model output based on theidentified relationship.

Example 10 is the agricultural work machine of any or all previousexamples, wherein the prior information map includes, as the values ofthe seeding characteristic, population values corresponding to thedifferent geographic locations in the field, and wherein the predictivemodel generator is configured to identify a relationship between thepopulation values and the machine characteristic based on the value ofthe machine characteristic corresponding to the geographic location andthe population value, in the prior information map, at the geographiclocation, the predictive characteristic model being configured toreceive a population value as a model input and generate a predictivevalue of the machine characteristic as a model output based on theidentified relationship.

Example 11 is a computer implemented method of generating a functionalpredictive agricultural map, comprising:

receiving a prior information map, at an agricultural work machine, thatincludes values of a seeding characteristic corresponding to differentgeographic locations in a field;

detecting a geographic location of the agricultural work machine;

detecting, with an in-situ sensor, a value of an agriculturalcharacteristic corresponding to the geographic location;

generating a predictive agricultural model that models a relationshipbetween the agricultural characteristic and the seeding characteristic;and

controlling a predictive map generator to generate the functionalpredictive agricultural map of the field, that maps predictive values ofthe agricultural characteristic to the different geographic locations inthe field based on the values of the seeding characteristic in the priorinformation map and based on the predictive agricultural model.

Example 12 is the computer implemented method of any or all previousexamples, and further comprising:

configuring the functional predictive agricultural map for a controlsystem that generates control signals to control a controllablesubsystem on the agricultural work machine based on the functionalpredictive agricultural map.

Example 13 is the computer implemented method of any or all previousexamples, wherein receiving a prior information map comprises:

receiving a seeding map that includes, as values of the seedingcharacteristic, genotype values corresponding to the differentgeographic locations in the field.

Example 14 is the computer implemented method of any or all previousexamples, wherein generating a predictive agricultural model comprises:

identifying a relationship between the genotype values and theagricultural characteristic based on the value of the agriculturalcharacteristic detected by the in-situ sensor corresponding to thegeographic location and the genotype value, in the seeding map, at thegeographic location; and

controlling a predictive model generator to generate the predictiveagricultural model that receives a genotype value as a model input andgenerates a predictive value of the agricultural characteristic as amodel output based on the identified relationship.

Example 15 is the computer implemented method of any or all previousexamples, wherein receiving a prior information map comprises:

receiving a seeding map that includes, as values of the seedingcharacteristic, population values corresponding to the differentgeographic locations in the field.

Example 16 is the computer implemented method of any or all previousexamples, wherein generating a predictive agricultural model comprises:

identifying a relationship between the population values and theagricultural characteristic based on the value of the agriculturalcharacteristic detected by the in-situ sensor corresponding to thegeographic location and the population value, in the seeding map, at thegeographic location; and

controlling a predictive model generator to generate the predictiveagricultural model that receives a population value as a model input andgenerates a predictive value of the agricultural characteristic as amodel output based on the identified relationship.

Example 17 is the computer implemented method of any or all previousexamples, further comprising:

controlling an operator interface mechanism to present the functionalpredictive agricultural characteristic map.

Example 18 is an agricultural work machine, comprising:

a communication system that receives a seeding map that indicates valuesof a seeding characteristic corresponding to different geographiclocations in a field;

a geographic position sensor that detects a geographic location of theagricultural work machine;

an in-situ sensor that detects a value of an agriculturalcharacteristic, corresponding to the geographic location;

a predictive model generator that generates a predictive model thatidentifies a relationship between the seeding characteristic and theagricultural characteristic based on a seeding characteristic value inthe seeding map at the geographic location and the value of theagricultural characteristic detected by the in-situ sensor correspondingto the geographic location; and

a predictive map generator that generates a functional predictive map ofthe field, that maps predictive values of the agriculturalcharacteristic to the different geographic locations in the field, basedon the values of the seeding characteristic in the seeding map and basedon the predictive model.

Example 19 is the agricultural work machine of any or all previousexamples, wherein the seeding map includes, as the values of the seedingcharacteristic, genotype values corresponding to the differentgeographic locations in the field, and wherein the predictive modelgenerator is configured to identify a relationship between the genotypevalues and the agricultural characteristic based on the value of theagricultural characteristic detected by the in-situ sensor correspondingto the geographic location and the genotype value, in the seeding map,at the geographic location, the predictive model being configured toreceive a genotype value as a model input and generate a predictivevalue of the agricultural characteristic as a model output based on theidentified relationship.

Although the subject matter has been described in language specific tostructural features or methodological acts, it is to be understood thatthe subject matter defined in the appended claims is not necessarilylimited to the specific features or acts described above. Rather, thespecific features and acts described above are disclosed as exampleforms of the claims.

1. An agricultural system comprising: a communication system thatreceives a prior information map that includes values of a seedingcharacteristic corresponding to different geographic locations in afield; a geographic position sensor that detects a geographic locationof an agricultural work machine; an in-situ sensor that detects a valueof an agricultural characteristic corresponding to the geographiclocation; a predictive model generator that generates a predictiveagricultural model that models a relationship between the seedingcharacteristic and the agricultural characteristic based on a value ofthe seeding characteristic in the prior information map at thegeographic location and the value of the agricultural characteristicdetected by the in-situ sensor corresponding to the geographic location.a predictive map generator that generates a functional predictiveagricultural characteristic map of the field that maps predictive valuesof the agricultural characteristic to the different geographic locationsin the field based on the values of the seeding characteristic in theprior information map and based on the predictive agricultural model. 2.The agricultural system of claim 1, wherein the predictive map generatorconfigures the functional predictive agricultural characteristic map forconsumption by a control system that generates control signals tocontrol a controllable subsystem on the agricultural work machine basedon the functional predictive agricultural characteristic map.
 3. Theagricultural system of claim 1, wherein the in-situ sensor comprises: anoptical sensor configured to detect an image indicative of theagricultural characteristic.
 4. The agricultural system of claim 3,wherein the optical sensor is oriented to detect an image of at least aportion of the field and further comprises: an image processing systemconfigured to process the image to identify the value of theagricultural characteristic in the image indicative of the agriculturalcharacteristic.
 5. The agricultural system of claim 1, wherein thein-situ sensor is configured to detect, as the value of the agriculturalcharacteristic, a value of a non-machine characteristic corresponding tothe geographic location.
 6. The agricultural system of claim 5, whereinthe prior information map includes, as the values of the seedingcharacteristic, genotype values corresponding to the differentgeographic locations in the field, and wherein the predictive modelgenerator is configured to identify a relationship between the genotypevalues and the non-machine characteristic based on the value of thenon-machine characteristic corresponding to the geographic location andthe genotype value, in the prior information map, at the geographiclocation, the predictive characteristic model being configured toreceive a genotype value as a model input and generate a predictivevalue of the non-machine characteristic as a model output based on theidentified relationship.
 7. The agricultural system of claim 5, whereinthe prior information map includes, as the values of the seedingcharacteristic, population values corresponding to the differentgeographic locations in the field, and wherein the predictive modelgenerator is configured to identify a relationship between thepopulation values and the non-machine characteristic based on the valueof the non-machine characteristic corresponding to the geographiclocation and the population value, in the prior information map, at thegeographic location, the predictive characteristic model beingconfigured to receive a population value as a model input and generate apredictive value of the non-machine characteristic as a model outputbased on the identified relationship.
 8. The agricultural system ofclaim 1, wherein the in-situ sensor is configured to detect, as thevalue of the agricultural characteristic, a value of a machinecharacteristic corresponding to the geographic location.
 9. Theagricultural system of claim 8, wherein the prior information mapincludes, as the values of the seeding characteristic, genotype valuescorresponding to the different geographic locations in the field, andwherein the predictive model generator is configured to identify arelationship between the genotype values and the machine characteristicbased on the value of the machine characteristic corresponding to thegeographic location and the genotype value, in the prior informationmap, at the geographic location, the predictive characteristic modelbeing configured to receive a genotype value as a model input andgenerate a predictive value of the machine characteristic as a modeloutput based on the identified relationship.
 10. The agricultural systemof claim 9, wherein the prior information map includes, as the values ofthe seeding characteristic, population values corresponding to thedifferent geographic locations in the field, and wherein the predictivemodel generator is configured to identify a relationship between thepopulation values and the machine characteristic based on the value ofthe machine characteristic corresponding to the geographic location andthe population value, in the prior information map, at the geographiclocation, the predictive characteristic model being configured toreceive a population value as a model input and generate a predictivevalue of the machine characteristic as a model output based on theidentified relationship.
 11. A computer implemented method of generatinga functional predictive agricultural map, comprising: receiving a priorinformation map that includes values of a seeding characteristiccorresponding to different geographic locations in a field; detecting ageographic location of an agricultural work machine; detecting, with anin-situ sensor, a value of an agricultural characteristic correspondingto the geographic location; generating a predictive agricultural modelthat models a relationship between the agricultural characteristic andthe seeding characteristic; and controlling a predictive map generatorto generate the functional predictive agricultural map of the field,that maps predictive values of the agricultural characteristic to thedifferent geographic locations in the field based on the values of theseeding characteristic in the prior information map and based on thepredictive agricultural model.
 12. The computer implemented method ofclaim 12, and further comprising: configuring the functional predictiveagricultural map for a control system that generates control signals tocontrol a controllable subsystem on the agricultural work machine basedon the functional predictive agricultural map.
 13. The computerimplemented method of claim 11, wherein receiving a prior informationmap comprises: receiving a seeding map that includes, as values of theseeding characteristic, genotype values corresponding to the differentgeographic locations in the field.
 14. The computer implemented methodof claim 13, wherein generating a predictive agricultural modelcomprises: identifying a relationship between the genotype values andthe agricultural characteristic based on the value of the agriculturalcharacteristic detected by the in-situ sensor corresponding to thegeographic location and the genotype value, in the seeding map, at thegeographic location; and controlling a predictive model generator togenerate the predictive agricultural model that receives a genotypevalue as a model input and generates a predictive value of theagricultural characteristic as a model output based on the identifiedrelationship.
 15. The computer implemented method of claim 11, whereinreceiving a prior information map comprises: receiving a seeding mapthat includes, as values of the seeding characteristic, populationvalues corresponding to the different geographic locations in the field.16. The computer implemented method of claim 15, wherein generating apredictive agricultural model comprises: identifying a relationshipbetween the population values and the agricultural characteristic basedon the value of the agricultural characteristic detected by the in-situsensor corresponding to the geographic location and the populationvalue, in the seeding map, at the geographic location; and controlling apredictive model generator to generate the predictive agricultural modelthat receives a population value as a model input and generates apredictive value of the agricultural characteristic as a model outputbased on the identified relationship.
 17. The computer implementedmethod of claim 11, further comprising: controlling an operatorinterface mechanism to present the functional predictive agriculturalcharacteristic map.
 18. An agricultural system, comprising: acommunication system that receives a seeding map that indicates valuesof a seeding characteristic corresponding to different geographiclocations in a field; a geographic position sensor that detects ageographic location of an agricultural work machine; an in-situ sensorthat detects a value of an agricultural characteristic, corresponding tothe geographic location; a predictive model generator that generates apredictive model that identities a relationship between the seedingcharacteristic and the agricultural characteristic based on a seedingcharacteristic value in the seeding map at the geographic location andthe value of the agricultural characteristic detected by the in-situsensor corresponding to the geographic location; and a predictive mapgenerator that generates a functional predictive map of the field, thatmaps predictive values of the agricultural characteristic to thedifferent geographic locations in the field, based on the values of theseeding characteristic in the seeding map and based on the predictivemodel.
 19. The agricultural system of claim 18, wherein the seeding mapincludes, as the values of the seeding characteristic, genotype valuescorresponding to the different geographic locations in the field, andwherein the predictive model generator is configured to identify arelationship between the genotype values and the agriculturalcharacteristic based on the value of the agricultural characteristicdetected by the in-situ sensor corresponding to the geographic locationand the genotype value, in the seeding map, at the geographic location,the predictive model being configured to receive a genotype value as amodel input and generate a predictive value of the agriculturalcharacteristic as a model output based on the identified relationship.20. The agricultural system of claim 18, wherein the seeding mapincludes, as the values of the seeding characteristic, population valuescorresponding to the different geographic locations in the field, andwherein the predictive model generator is configured to identify arelationship between the population values and the agriculturalcharacteristic based on the value of the agricultural characteristicdetected by the in-situ sensor corresponding to the geographic locationand the population value, in the seeding map, at the geographiclocation, the predictive model being configured to receive a populationvalue as a model input and generate a predictive value of theagricultural characteristic as a model output based on the identifiedrelationship.